A Recommendation Model Based on Multi-Emotion Similarity in the Social Networks

Transcription

1 information Articl A Rcommndation Modl Basd on Multi-Emotion Similarity in Social Ntworks Jun Long, Yulou Wang, Xinpan Yuan 2, *, Ting Li and Qunfng Liu School Information Scinnd Enginring, Cntral South Univrsity, Changsha 483, China; jlong@csu.du.cn (J.L.); @csu.du.cn (Y.W.); tinglicsu@csu.du.cn (T.L.); qunfngliu@csu.du.cn (Q.L.) 2 School Computr, Hunan Univrsity Tchnology, Zhuzhou 42, China * Corrspondnc: xpyuanfly@63.com; Tl.: Rcivd: 29 Novmbr 28; Accptd: January 29; Publishd: 6 January 29 Abstract: This papr proposd a rcommndation modl calld RM-SA, which is basd on multi-motional analysis in ntworks. In RM-MES schm, rcommndation valus goods ar primarily drivd from probabilitis calculatd by a similar xisting rcommndation systm during initiation stag rcommndation systm. First, bhaviors thos usrs can b dividd into thr aspcts, including browsing goods, buying goods only, and purchasing valuating goods. Thn, charactristics goods and motional information usr ar considrd to dtrmin similaritis btwn usrs and stors. W chos most similar shop as rfrnc xisting shop in xprimnt. Thn, rcommndation probability matrix both xisting stor and nw stor is computd basd on similaritis btwn usrs and targt usr, who ar randomly slctd. Finally, w usd co-purchasing mtadata from Amazon and a crtain kind commnts to vrify ffctivnss and prformanc RM-MES schm proposd in this papr through comprhnsiv xprimnts. Th final rsults showd that prcision, rcall, and F -masur wr incrasd by 9.7%, 2.73% and 2.2% rspctivly. Kywords: rcommndation modl; social ntwork; multi-motion; cold start. Introduction In rcnt yars, popl hav bn doing mor onlin shopping on sits such as Amazon, Taobao, and Jingdong. As a rsult, how to build up an ffctiv rcommndation modl is bcoming a crucial rsarch projct []. Rcommndation systms in social ntworks wr first proposd by Rsnick P and Varian HR in 997 and usd to provid prsonalizd and intllignt information srvics to usrs on onlin shopping sits. A varity rcommndation systms hav bn proposd by rsarchrs. Th main rcommndation systms includ [2 5]: () Contnt-basd rcommndation systms, which will rcommnd goods that a usr is intrstd in basd on ir historical bhaviors; (2) collaborativ filtring rcommndation systms, which adopt similaritis usrs historical purchasing bhaviors to bttr rprsnt rcommndation procss in social ntworks; (3) hybrid rcommndation systms. Th onlin shopping wbsit tn taks advantag multipl mthods to improv its rcommndation ability. To shop ownr, it is important that a rcommndation systm can ffctivly introduc products with potntial purchasing powr to usrs. Although r ar various mthods which hav bn rcommndd in prvious studis, som significant furr difficultis ar still ndd to b ovrcom. For xampl, cold start problm still xists in rcommndation schms and is not asy to b solvd ffctivly. Whn a nw shop opns, although it dos not hav purchas rcords, rlationships among goods ar stablishd by rfrring to idntical products Information 29,, 8; doi:.339/info8

2 Information 29,, slctd xisting shop. Th xisting stor is on that has bn running for som tim and whos historical purchas rcords ar rich. For nwly opnd stors with nonxistnt or spars transaction rcords, it is difficult for rcommndation systm to mak an ffctiv rcommndation. Thrfor, xisting stor that is most similar to targt stor is chosn as rfrnc xisting stor in papr. Th calculation similarity is numbr goods in nw stor dividd by that in xisting stor. Th rfrnc xisting stor shars maximum numbrs goods with targt stor; rfor, w can rcommnd goods to targt usrs according to rfrnc xisting stor. Emotion, as an indispnsabl psychological activity in social ntworks, always affcts daily livs and dcision-making procsss usrs in shopping. This papr proposs a rcommndation modl (RM-MES) basd on multi-motion similarity in social ntworks. Th problm studid in this papr targts a particular stor and rgards how to ffctivly rcommnd goods to usrs to maximiz bnfits shop ownr and how to improv prformanc indxs rcommndd schm, such as prcision, rcall, and F -masur. Our main contributions this schm ar as follows: () To solv cold start problm, RM-MES schm uss historical purchas rcords an xisting stor to guid a rcntly opnd stor, which aims to form a rcommndation probability matrix both xisting stor and nw stor for targt usrs; (2) To improv accuracy rcommndation rsults, w propos a schm basd on multi-motional analysis. Th LDA topic modl is usd to subdivid usr valuation into six indxs. Considring usr prfrncs for diffrnt lvls goods, similarity usrs is dply analyzd, and similarity rsults show its advantags; (3) With considrations diffrnt prformancs usrs, bhaviors thos usrs can b dividd into thr aspcts, including browsing goods, buying goods only, and purchasing valuating goods. According to thr catgoris, browsing similarity, purchasing similarity, and motional similarity among usrs can b idntifid; (4) W adopt mtadata Amazon goods to vrify ffctivnss and prformanc RM-MES schm through comprhnsiv xprimnts. In addition, w analyz impact transition probability influnc factor α through xprimnts. 2. Rlatd Works Gnrally, rcommndation systms us a crtain algorithm basd on usr bhavior data or itm data to rcommnd itms that usrs nd. According to diffrncs rcommndation algorithms, rcommndation systms can b dividd into following catgoris [6 ]: () Contnt-basd rcommndation systms. According to itms that usrs hav likd, a contnt-basd systm can rcommnd similar itms to usrs. Such systms wr dvlopd basd on information rtrival and filtring, using historical purchas rcords targt usrs or analysis charactristics from purchas information via statistics and machin larning. Chn t al. [2] proposd a probabilistipproach on basis TruSkill for contnt-basd rcommndation systms. This systm is usful for handling high uncrtainty bcaus it is only basd on availabl goods and ratings givn by usrs. Thr ar still som disadvantags, such as limitd contnt analysis and nw usr problm. (2) Collaborativ filtring rcommndation systms, which ar on most widly-usd mthods in practical applications, and ir practical applications includ Amazon, Taobao, and Digg. Ths typs schms rcommnd products basd on or usrs that hav rlationships that ar similar to thos targt usr. Li t al. [3] dsignd a trust-awar rcommndr systm, which fully xtractd influnc trust information and contxtual information on ratings to improv prcision. Wang t al. [4] dsignd a combination modl composd rcommndr and similarity masur. H t al. [5] proposd a novl modl for on-class collaborativ filtring stting, which combins high-lvl visual faturs xtractd from a dp convolutional nural ntwork, usrs past fdback, as wll as volving trnds within community to uncovr complx and

3 Information 29,, volving visual factors that popl considr whn valuating products. Sun t al. [6] proposd a tim-snsitiv collaborativ filtring mthod to discovr latst prfrncs customrs and improv accuracy rcommndation systm without complicating training phas. As a typical rcommndation mthod, collaborativ filtring rcommndation systms still hav som problms that nd to b addrssd, such as spars databas problm and cold start problm. (3) Hybrid rcommndation systms, which combin advantags ach rcommndation schm. As ach rcommndation schm is not prfct, hybrid rcommndation systms ar frquntly usd in practical applications. Not all combination mthods ar ffctiv in practical applications. It is important to avoid or compnsat for waknsss ir rcommndation. In combination mthod hybrid rcommndation systms, rsarchrs hav proposd svn idas combination: Wight, switch, mixd, fatur combination, cascad, fatur augmntation, and mta-lvl. Song t al. [7] rsarchd how to gain bttr rcommndations traditional rcommndation modls on basis rlationship information in social ntworks btwn customrs and shops and proposd a matrix dcomposition framwork basd on intgrating rlationship information in social ntworks. Emotion, as an indispnsabl psychological activity in social ntworks, always affcts daily livs and dcision-making procsss usrs. Rcommndations basd on motion gt much attntion from rsarchrs in fild prsonalizd rcommndation [8 22]. Guo-Qiang t al. [23] built a collaborativ filtring rcommndation algorithm basd on usr motion and combind usr ratings and motional commnts togr through subjct xtraction and sntimnt analysis in usrs projct rviws. Wijayanti t al. [24] proposd an nsmbl a machin larning approach to dtct sntimnt polarity in usr-gnratd txt. Vagliano t al. [25] proposd a rcommndation mthod according to smantinnotation ntitis that ar rcordd in customr commnts, and ntitis ar considrd as candidat rcommndations. Musto t al. [26] dsignd a multi-critria collaborativ filtring mthod, which uss aspct-basd sntimnt analyss usrs rviws to obtain sntimnt scors as ratings itms from usrs. Contratrs t al. [27] proposd a rcommndation procss that includs sntimnt analysis to txtual data xtractd from Facbook and Twittr and prsntd rsults an xprimnt in which this algorithm was usd to rduc cold start issu. So t al. [28] proposd a frindship strngth-basd prsonalizd rcommndr systm. Th prsonalizd rcommndr systm grants a wight to thos usrs who ar closly connctd in ir social circl basd on frindship strngth in ordr to rcommnd topics or activitis in which usrs might b intrstd. Mng t al. [29] providd a principld and mamatical way to xploit both positiv and ngativ motion on rviws and proposd a novl framwork MIRROR, xploiting motion on rviws for rcommnd systms from both global and local prspctivs. Ths schms abov hav furr improvd ffctivnss rcommndation algorithms. Howvr, s rcommndation mthods still hav som problms that nd to b ovrcom:. Most rcommndation schms only considr cold start problm nw usrs, but do not considr cold start problm for a rcntly opnd stor, so as to affct rcommnd quality rcommndation systm; 2. Som rcommndation schms sarch for usr prfrncs by xtracting usr Facbook and Twittr data. Howvr, it is difficult to xtract usr s prsonal information du to issus such as prmissions and tchnology. Additionally, bcaus information that includs usr motions is tn incomplt and fuzzy, it is not asy to dirctly analyz motions in information from Facbook and Twittr; 3. Ths rcommndation systms basd on motion only considr positiv and ngativ motions but do not considr usrs prfrncs in or aspcts; 4. Whn calculating similaritis usrs bhaviors, most rcommndation schms do not tak corrlation btwn projcts into considration; 5. Most rcommndation schms fail to considr trust factor ach pic mrchandis, which may caus rcommndation systm to provid distrustd itms to targt usrs.

4 Information 29,, Th RM-MES Algorithm In RM-MES schm, st usrs is dfind as C = (c, c 2,..., c n ), st shops is dfind as S = (s, s 2,..., s n ), st goods in nw targt shop is dfind as I = (I, I 2,..., I n ), st goods in rfrnc xisting shop is dfind as r f = (r, r 2,..., r n ), and st rviws usrs is dfind as C i = (c i, c i2,..., c in ). In this papr, rlvant notations ar shown in Tabl. Symbol Dscription Tabl. Main notations. Sim c[a] Th st similar usrs to targt usr a SMX l[a] Th purchas matrixs similar usrs λi,j S Th rlationship among good i and good j Th proportion man rcommndation probability n Th numbr final purchass in nw shop List i Th numbr rcommndd goods in ach round w Th lngth tim window y Th proportion influnc factor trust z Th proportion influnc factor latnt factor H[i][j] Th rcommndation matrix targt usr basd on similarity usrs Th rcommndation matrix targt usr basd on corrlation rlationship among goods S[i][j] A[i][j] Th rcommndation probability matrix targt usr basd on M[i][j] and S[i][j] trust[i] Th valu trust for good i rp[i] Th rputation good i f r[i] Th purchas frquncy good i µ Th proportion rcommndation probability for nw shop rcall Th probability that usrs purchas what y lik in rcommndation list F masur Th standard masurmnt for classification accuracy a rcommndation algorithm B i Th numbr goods that usr i liks Th numbr goods that usr i has purchasd in rcommndation list N i 3.. Sarch for Existing and Similar Rfrnc Usrs in Existing Shop for Tagt Usr 3... Th Calculation Mthod for Similar Shops First, RM-MES schm should sarch xisting stor that is most similar to targt stor for rfrnc, which mans finding largst numbr goods that targt stor and xisting stor both hav in common. Ths typical stors can b calculatd with Equation () blow: R[i] = num(s) num(s i) num(s) num(s i ), () whr num(s i ) rprsnts numbr goods that xisting shop S i has and num(s) rprsnts numbr goods that nw targt stor has. In xprimnt, w chos most similar stor S i for rfrnc, which mans sarching maximum rsult R[i] according to Equation () Emotional Analysis Usr Rviws Th first stp is data prprocssing. Th rviws usr ar first catgorizd on basis ir attributs. Latnt Dirichlt allocation (LDA) [2] has bn mployd as a tchniqu to idntify and annotat larg txt corpora with concpts, to track changs in topics ovr tim, and to assss similarity btwn documnts. Th LDA topic modls provid idntification cor topics from a providd txt collction. By analyzing LDA matic modl 5 onlin rviws, w found that most consumrs pay attntion to six indicators: Quality, pric, apparanc, configuration, srvic, and xprss dlivry. W thus classifid rviws usrs into six rspctiv catgoris.

5 Information 29,, Th scond stp is to xtract motional information from rviws usrs. This includs xtraction and discrimination valuation words, xtraction valuation objcts, xtraction combination valuation units, xtraction valuation phrass, and xtraction valuation collocations. Thn, basd on an motional lxicon, w analyzd usr motional polarity and obtaind motional valus. To distinguish words with sam motional tndncis and diffrnt motional polarity, w obtaind motional scors motional words according to public motional vocabulary HowNt ( Th HowNt is popular du to its contxt-spcific lxicons. Thr ar thr catgoris words: Emotional words, dgr words, and ngativ words. Ngativ words can b usd to dtrmin whr polarity a commnt is rvrsd or not. Dgr words can provid diffrnt scors to diffrnt motional words, and motional words can b dividd into positiv words and ngativ words. If an motional word is not in HowNt or has no motional valu, n w found its synonyms on basis TongYiCi Cilin (Mi t al., proposd in 983) and comput rlvant motional scor. Th txt grading formula, as shown in Equation (2): Scor(i) = n j ( ) t k word(j) (2) whr Scor(i) rprsnts scor ach commnt, indx t dpnds on polarity rvrsal, k rprsnts dgr dgr word, and word(j) is original scor vry word. Finally, w computd valu commnt: Rp i = ω Scor(quality) + ω 2 Scor(pric) + ω 3 Scor(apparanc) + ω 4 Scor(con f iguration) + ω 5 Scor(srvic) + ω 6 Scor(dlivry), (3) whr Rp i rprsnts rputation commodity, Scor rprsnts motional scor valuation, and ω i rprsnts wight ach indx Th Calculation Mthod for Similar Usrs According to flow information in social ntworks, targt usr is randomly slctd by RM-MES schm, and n w nd to find usrs that ar similar to targt usr. Whn considring similarity usr bhavior, most schms ignor diffrnt prformanc usrs; rfor, prcision rcommndation rsults may not b satisfactory. Aftr taking into account diffrnt prformancs usrs, w dividd usrs bhaviors into browsing goods, buying goods, and purchasing goods as wll as valuating s goods. Thn, w obtaind similarity ir browsing and purchasing bhaviors and motional flings among two usrs. To obtain similarity btwn and targt usr c b, w first obtaind similarity ir browsing. Th browsing similarity formulas ar as follows: Sim a,b = Sim(Brows( ), Brows(c b )) = Brows() Brows(c b ) Brows( ) Brows(c b ), (4) whr Brows( ) rprsnts goods that usr has browsd and Brows(c b ) rprsnts goods that usr c b has browsd. To obtain similarity ir purchasing btwn usr and targt usr c b, Equation (5) can b obtaind as follows: k Sa,b Sim2 a,b = f r 2 k (r a,k r i ) ( r b,k r j ) ϕ a ϕ b. (5) Th similarity motional flings btwn usr c i and targt usr c j, Equation (6) can b obtaind as follows:

6 Information 29,, Sim3 a,b = k Sa,b λ + ( λ) (r rp 2 k f r 2 a,k r i ) ( ) r b,k r j k, (6) ϕ a ϕ b whr Simi a,b rprsnts corrlation motional similarity btwn usr and usr c b, and rp k and fr k rspctivly rprsnt rputation and frquncy good I k. S a,b rprsnts st goods that wr purchasd both by usr and usr c b. r i and r j rspctivly rprsnt man ratings usr and usr c b, rspctivly. r a,k and r b,k ar ratings usr and c b for good I k. ϕ a and ϕ b rspctivly rprsnt standard dviations for usr and c b, and calculation mthod is shown by Equation (7) blow: k Sa,b ( ) 2 ϕ a = r j,a r i ) (7) ( ) 2 ϕ b = k Sa,b r j,b r j ) Sim a,b = δ Sim a,b + δ 2 Sim2 a,b + δ 3 Sim3 a,b, (8) whr, δ i rprsnts wight indx diffrnt similarity, rspctivly. W can gt similarity dgr btwn usrs and c b according to Equation (8) ( highr, bttr). Th st similar usrs for targt usr is dfind as Sim c[a] and thrshold numbr similar usrs is st as q in xprimnt. Thus, w dfind datast similar usrs for targt usr as Sim c[a] = (c, c 2,..., c n ) Establishmnt Rcommndation Modl Th Rcommndation Probability for Each Good According to Historical Purchas Rcords To rcommnd goods to usrs mor ffctivly, w ndd to calculat rcommndation probability ach mrchandis on basis purchas rcords usrs. Th main calculation mthods ar illustratd blow: Suppos that past stats ar V = x, V = x,..., V t = x t and that prsnt stat is V t = x t, whr V t = x t rprsnts stat bing x t at tim t; valu x t is or. In this cas, stat probability at nxt tim stp x t+ is rprsntd by Equation (9): p(v t+ = x t+ V t = x, V t = x t,..., V t m+ = x t m+ ), (9) whr p rprsnts probability stat at nxt tim. Thrfor, w can obtain probability rcommndation matrix both xisting stor for rfrnnd nw stor for targt usr. For xampl, in ordr to gt rcommndation probability matrix rfrnc xisting stor for targt usr, transfr matrix can b illustratd as Equation () blow: Hu a = g, g,2... g,n g2, g2,2... g2,n g n, g n,2 g n,n, () whr Hc a rprsnts rcommndation probability matrix ach pic mrchandis for targt usr, and rprsnts purchas rcords in typical stor for similar usrs and targt usr. g a,b rprsnts probability that targt usr will purchas good I j at nxt tim instant t + in condition that historical purchas rcords ar and targt usr has purchasd good I i at currnt tim t. Th calculation mthod g a,b is shown as Equation () blow: g a,b = p( i t+ B c b t+ B b ) p ( a t+ B c b t = t+ a ) Bc b t p ( a B c ) = a t num(c(t t + )), () num(c(t))

7 Information 29,, whr B t+ rprsnts st goods that usr will purchas at tim t + and B t indicats st goods that usr has purchasd at tim t. num(c(t)) rprsnts numbr usrs that hav purchasd good I i at tim t, and num(c(t t + )) rprsnts numbr usrs that hav purchasd good I i at tim t and purchasd product I j at tim t +. At bginning xprimnt, nwly opnd stor has almost no historical purchas rcords; rfor, it is difficult to find similar usrs for targt usr. Along with xprimntal training, purchas rcords nw stor ar gradually incrasing, and w can rfor sarch for similar usrs to targt usr, and calculation mthod is sam as abov. For instanc, assum that r is a targt usr and that similar usrs can first b obtaind basd on purchas rcords in rfrnc xisting stor; aftr that, w can gt rcommndation probability matrix targt usr. Suppos thrshold n = 4, tim window w = 3 and numbr good 5. Th historical purchas rcords similar usrs in rfrnc xisting stor whn tim window m 3 ar shown blow: k : k 2 : k 3 : k 4 : whr row matrix k i rprsnts numbr goods and column matrix k i rprsnts cas historical purchasd X j. If a usr has purchasd good I 3 at tim 2, rsult row 3 and column 2 is. Orwis, rsult row 3 and column 2 is. Th historical purchas rcords targt usr ar shown blow: k : According to Equation (), rsult rcommndation probability matrix can b computd blow: Hu a = In ordr to xplain rsults abov, w computd rsult g3, as an illustration. It is clar that numbr usrs btwn both similar usrs and targt usr that hav vr purchasd I 3 it is 5 at tim t. Thus, dnominator rsult is 5. In cas that usrs hav purchasd good I 3, numbr usrs that purchas good I is at tim t +. Thus, numrator rsult is. Thrfor, w can gt rsult for g3, is Th Calculation Mthod for Corrlation Rlationships btwn Goods Howvr, it is ncssary to considr corrlation rlationships among goods in rcommndation systms. According to charactristics goods and catgoris y blong to, rlationships among goods ar takn into considration on basis information flow on Intrnt, which mans that if flow information is largr, corrlation rlationship among itms is closr..,

8 Information 29,, In our papr, S = (s i,j ) is dfind as corrlation rlationship btwn goods in schm, whr s i,j rprsnts probability corrlation rlationship good I i and good I j. According to dfinition s i,j, it can b sn that valu s i,j is in intrval (, ). Th matrix corrlation rlationship goods is illustratd as follows: S = s, s,2... s,n s 2, s 2,2... s 2,n s n, s n,2 s n,n. (2) Aftr that, w calculatd rsult ach S i,j. B i,j is dfind as numbr usrs that hav bought both good I i and good I j. Th computing mthod S i,j is illustratd as follows: S i,j = λ S i,j h( B i,j ) = λ S i,j + B i,j, (3) whr λ S i,j rprsnts whr r is a rlationship among good I i and good I j. If r is a rlationship among I i and I j, rsult λ S i,j is. Orwis, rsult λs i,j is. h( B i,j ) is a logical function that can qualify rsult S i,j during intrval [, ]. It can b sn that rsult S i,j is symmtric, which mans that valu S i,j is qual to that S j,i. W suppos that r is a corrlation rlationship btwn I and I 2 and btwn I 3 and I 5. If B,2 = 4 and B 3,5 = 2, n w can obtain rsults that s,2 = s 2, =.892, s 3,5 = s 5,3 =.889, and ors in rcommndation probability matrix S ar. Th rsults for rcommndation matrix rlationship corrlations btwn goods ar shown blow: S = Th Man Rcommndation Probability Matrix Goods.889 Thn, combination rcommndation probability matrix can b obtaind in rfrnc xisting stor, and calculation mthod is as follows: A c i = h H c i + ( h) S, (4) whr A c i rprsnts final rcommndation probability matrix and h rprsnts influnc factor. Th rcommndation probability matrix is as shown blow: A c i = b, b2, b3, b4, b,2 b2, b3,2 b4,2 b,3 b2,3 b3,3 b4,3 b,4 b2,4 b3,4 b4,4 b,5 b5, b3,5 b4,5., (5) b 5, b 5,2 b 5,3 b 5,4 b 5,5 whr bi,j rprsnts rcommndation probability ach good aftr adding factor corrlation rlationship btwn goods into Hc i. According to Equation (4), if h =.6, according to rsults Hu i and S, w can gt rsults final probability rcommndation matrix A c i with data similar usrs and corrlation

9 Information 29,, rlationships among goods in xprimnt. Th probability rcommndation matrix A c i is shown as follows: A u i = Thrfor, on basis matrix analysis abov, w can obtain man transition probability ach good in rfrnc xisting stor. Th calculatd mthod is dnotd as Equation (6): p ( i t+ B c ) a t+ = B p ( i t+ B t+ B ) t, (6) t a B ua t whr B t rprsnts numbr goods that targt usr has bought at tim t. Basd on abov xampl and Equation (6), final rcommndation probability ach good in xisting stor is: p ( I B c ) a t+ I4 =.24 =.2 p ( I 2 B c ) a t+ I4 = = p ( I 3 B c ) a t+ I4 =.24 =.36 p ( I 4 B c ) a t+ I4 = = p ( I 5 B t+ ) I 4 = =.24. Whn a nw stor opns, although it dos not hav purchas rcords, rlationships btwn goods can b dtrmind by rfrring to thos xisting stor for rfrnc Th Trust Factor Goods in RM-MES Schm In traditional rcommndation schms, r xists a dpndncy among usrs in social ntworks. If two usrs hav a similar prformanc, trust lvl is obviously high. Thrfor, in this papr, trust factor is addd into RM-MES schm to improv accuracy rcommndation rsults. In RM-MES schm, trust factor a good is dividd into rputation, sals rank, and frquncy. Th calculatd mthod trust is dnotd as follows: trust i = τ rp i + θ rank i + ( τ θ) f r i Fr, (7) whr trust i rprsnts trust dgr good i, rp i rprsnts rputation good I, and f r i rprsnts purchasd frquncy good i. Fr is a constant in xprimnt, which maks sur that valu f r i /Fr is in intrval [, ]. τ and θ rspctivly rprsnt scal factor rputation and influnc factor sals rank for good I i. rank i is sals rank good I i. Bcaus historical purchas rcords ar rich in xisting stor for rfrnc, valu rp i, rank i, and f r i for good i is crtain. With opration RM-MES schm, rputation, sals rank, and purchas frquncy in rcntly opnd stor chang at diffrnt tim cycls Th Latnt Factors Usrs in RM-MES Schm In xisting stor, it is asy to dtrmin targt usr s transition matrix probability by historis browsing and trust factor goods if targt usr is not nw. Howvr, r ar fw historis browsing and trust dgrs goods for a rcntly opnd stor, which is calld a

10 Information 29,, 8 8 cold start. In our papr, w dfind L a = (ag, gndr, location, brows) as attribut st latnt factors. If targt usr is nw, and r is rfor no historical purchas rcord, it is not asy to rcommnd accurat goods for usr. Howvr, st latnt similar usrs can b adoptd to comput latnt goods that targt usr may lik. Th st similar usrs for targt usr is dfind as Sim(Latnt( ), Latnt( )), which can b computd by four factors shown in Equation (8). Sim(Latnt( ), Latnt(c b )) = δ Sim(Ag( ), Ag(c b )) + δ 2 Sim(Gndr( ), Gndr(c b )) + δ 3 Sim(Location( ), Location(c b )) + δ 4 Sim(Brows( ), Brows(c b )), (8) whr δ rprsnts influnc factor attribut similarity, δ + δ 2 + δ 3 + δ 4 =, Sim(Ag( ), Ag(c b )) rprsnts latnt similarity rlationship ag btwn and c b, Sim(Gndr(c), Gndr(c b )) rprsnts latnt similarity gndr btwn and c b, Sim(Location( ), Location(c b )) rprsnts latnt similarity location btwn and c b, and Sim(Brows( ), Brows(c b )) rprsnts latnt similarity browsing btwn and c b Th Establishmnt Combination Calculation Basd on mthods shown abov, RM-MES schm combins man rcommndation probability matrix goods for targt usrs, trust dgr slctd goods, and latnt factor targt usrs, to stablish computation mthod illustratd blow: R I j = x p ( i t+ B c ) i t+ + y trusti + z latnt I j, x + y + z =, (9) whr R I j rprsnts rcommndation probability in xisting slctd stor to rcommnd good I j to targt usr. x and y rprsnt, rspctivly, wight man probability matrix rcommndd goods for usr and trust dgr for good I j, and z is wight whn targt usr is nw. If targt usr slctd is nw, n historical purchas rcords ar mpty, and rfor x + y =, z =. In RM-MES schm, w calculatd R I j in both xisting stor and nw stor and n combin m togr to rcommnd appropriat goods for targt usrs. In nw shop, rcommndation probabilitis goods ar dfind as R I j. Th calculation mthod R I j is sam as that in R I j. Th calculation mthod for combination R I j and R I j can b obtaind by Equation (2) as follows: R I j f = µ R I j + ( µ) R I j, (2) whr R I j f rprsnts rcommndation probability providing good I j to usr, and µ is influnc factor historical purchas rcords in rcntly opnd stor. Th historical purchas rcords a rcntly opnd stor ar spars, and rfor rsult R I j for rcntly opnd stor is almost. Thus, at bginning our xprimnt, valu influnc factor µ was zro; with running rcntly opnd stor, historical purchas rcords in rcntly opnd stor will grow largr, and valu µ will incras. W can calculat valu influnc factor µ by Equation (2) as follows: µ = n i= f r i, (2) Fr whr n i= f r i rprsnts sum purchas frquncis goods I to I n in a tim priod and Fr is a constant numbr, which was dfind abov. With opration rcntly opnd stor, historical purchas rcord will incras; rfor, influnc factor µ will bcom largr. Whn n i= f r i rachs thrshold total purchas numbr n, stor can rcommnd goods to usrs on basis its own historical purchas rcords.

11 Information 29,, 8 8 Th psudo-cod RM-MES algorithm is shown in Algorithm. Algorithm. Th main RM-MES Algorithm Input: S, I, I i, n, m, d, q, a[i][j][k], a [i][j][k], f r[i], f r [i], x, y, z, µ, ω i, δ i Output: R I j f : for ach s i S 2: R[i] = num(i) num(i i), r f = max(r[i], r f ) num(i) num(i i ) 3: nd for 4: for ach c i C 5: for ach c ij c i 6: Scor = m j ( ) t k word(d) 7: nd for 8: Rp i = ω Scor(quality) + ω 2 Scor(pric) + ω 3 Scor(apparanc)+ ω 4 Scor(con f iguration) + ω 5 Scor(srvic) + ω 6 Scor(dlivry) 9: Calculat Sim a,i according to Equations (4) (8); : nd for : Rvrs ordr by Sim a,i and obtain Sim c[a] = ( ) c, c 2,..., c q ; 2: for ach c i Sim c[a] 3: Calculat transfr matrix Hc a according to Equations () and (); 4: Calculat transfr matrix S basd on rlationship: 5: S a,i = λ S a,i g( ) B a,i = λ S a,i + B a,i 6: nd for 7: Calculat final transfr matrix A basd on Hc a and S: 8: A = h Mc a + ( h) S 9: Calculat rcommndation probability at nxt tim instanc basd on historical purchas rcords ( a[i][j][k] ) usrs: 2: p i t+ B t+ = B ca t i B ca t 2: for ach r i r f ( p i t+ B ) t+ B t 22: Calculat trust dgr ach good: 23: trust i = τ rp i + θ + ( τ θ) f r i rank i Fr 24: Calculat latnt factor if targt usr is nw; 25: Comprhnsivly comput probability: R I ( ) j = x p i t+ B t+ + y trust i + z latnt I j 26: Combin rcommndation probabilitis rfrnc shop and nw shop: 27: R I j f = µ R I j + ( µ) R I j 28: nd for 29: Rturn R I j f 4. Exprimntal Evaluations and Rsults 4.. Exprimntal Sttings In ordr to valuat ffctivnss and prformanc RM-MES schm, purchasing ntwork mtadata Amazon products ( and usr rviw information wr usd in our xprimnts [,5]. First, to valuat ffctivnss and prformanc RM-MES schm, w closly compard RM-MES schm with classic trust-basd schm undr a crtain influnc factor in diffrnt tim priods. Thn, w vrifid influnc factor influnc factor x in RM-MES schm. In addition, w compard trust dgr ach slctd good undr diffrnt tim priods. Finally, w compard and analyzd various dtaild rsults during xprimnt. Th datast in xprimnt was obtaind by nquiring into datast on Amazon wbsit. W chos mtadata and rviws halth and prsonal car catgory, which contains approximatly 263,32 diffrnt goods. For ach usr, following information could b obtaind:

12 Information 29,, ID product bought, rviw ID, commnt on product, and rviw tim. For ach pic mrchandis, following information could b obtaind: ID, sals rank, catgoris, dscription, and list similar goods. From halth and prsonal car catalogu, in our xprimnt, w chos svral kinds goods with highr purchas ranking in xprimnts. In our xprimnts, w usd 3/4 slctd purchas rcords as training st and rst as tst st. Thn, to valuat ffctivnss and prformanc RM-MES schm, w compard rsults prcision, rcall, and F -masur. Th thr indxs abov ar thr standard masurmnts for masuring ffctivnss a rcommndation schm ( highr, bttr). Th rcall can b obtaind by following calculation mthod (Equation (22)): prcision = H H a= N a List a, (22) whr H rprsnts total numbr both targt usr and similar usrs, N a indicats numbr goods that usr purchasd in rcommndation list, and List a indicats numbr goods in rcommndation list. Th rcall in RM-MES schm can b computd by Equation (23) as follows: rcall = H H a= N a B a, (23) whr B a rprsnts numbr goods that usr liks on basis commnts givn by usr and rcall indicats numbr goods that targt usr liks in list rcommndation to total numbr goods that usr liks. Th biggr valu rcall, bttr. Bcaus F -masur is calculatd as a combination s two indicators, F -masur can comprhnsivly vrify ffctivnss RM-MES schm. Th calculation mthod F -masur can b obtaind by Equation (24) as follows: F masur = 2 rcall prcision prcision + rcall. (24) If F -masur rcommndation schm is highr, prformanc rcommndation schm is bttr Exprimntal Rsults In this sction, prformanc RM-MES schm is compard with that trust-basd schm proposd in Rfrnc []. Th trust-basd schm is to rcommnd appropriat goods to usrs basd on trust factor goods. W chos halth and prsonal car shop as rfrnc rcntly opnd shop. Th usrs slctd in xprimnts wr not nw, so latnt factor usrs was not considrd in our xprimnt. In or words, x + y = and z =. As shown in Figur a, prcision trust-basd schm was lowr than that RM-MES schm on avrag. Whn a nw stor opns, historical purchas rcords ar mor likly to b spars, and rfor prcision is low (cold start). Th RM-MES schm had a bttr prformanc than trust-basd modl on avrag. Whn tim is 8, rcommndation rsults prcision nwly opnd stor ar highr than thos xisting stor, which mans stor can rcommnd goods to usrs with its own historical purchas rcords.

13 trust-basd schm was lowr than that RM-MES schm on avrag. Whn a nw stor opns, historical purchas rcords ar mor likly to b spars, and rfor prcision is low (cold start). Th RM-MES schm had a bttr prformanc than trust-basd modl on avrag. Whn tim is 8, rcommndation rsults prcision nwly opnd stor ar highr than Information thos 29, xisting, 8 stor, which mans stor can rcommnd goods to usrs with its 3own 8 historical purchas rcords. Figur. Th rsult xprimnt. (a) Th rsults prcision in a halth and prsonal car shop; (b) prcntag improvmnt prcision in halth and prsonal car shop. From xprimntal rsults prcision, it is clar that prcision trust-basd schm is lowr than that RM-MES schm whn a stor is nwly opnd. This is bcaus RM-MES schm first rfrs to corrlation goods among xisting stor and nw stor and n guids nw stor to rcommnd goods to usrs. Thus, though nwly opnd stor has fw purchas rcords, RM-MES schm still still has has a bttr a bttr prformanc than than or or mthods. mthods. Thus, Thus, it canit b can sn b sn that that RM-MES RM-MES schm schm can solv can solv cold cold start start problm. problm. With running nwly opnd stor, r ar mor and mor purchas rcords in nw stor, so it is mor ffctiv for it to adopt its own purchas rcords. Thus, aftr a priod tim, prcision rcommndation modl modl will will b maintaind b at aat constant a constant lvl. lvl. It is similar It is similar to or to schms or schms that only that adopt only adopt ir purchas ir purchas rcords rcords to rcommnd to rcommnd goods. goods. Thus, Thus, prcision prcision ratio ratio RM-MES RM-MES schm schm is similar is similar to thatto that or schms. or schms. Th comparison prcntags improvmnt prcision whn tim <6 is shown in Figur b. It can b sn that prcntag improvmnts prcision is vry high at bginning. This is bcaus whn a nw shop opns, it has fw historical purchas rcords, so it is difficult to rcommnd goods for targt usrs appropriatly. Howvr, proposd mthod in our papr can combin both RM-MES schm and trust-basd rcommndation modl to rcommnd goods to targt usrs. Thus, prcnt improvmnts prcision ar vry high. Thn, w closly compard rsult rcall for two schms with tim passing, and rsults ar shown in Figur 2a. Figur 2b shows comparison prcnt improvmnts rcall in RM-MES schm. From rsults rcall, it is clar that RM-MES schm has bttr ffctivnss and prformanc than trust-basd schm at bginning. Whn a nw stor opns, historical purchas rcords is mor likly to b %; thus, it is difficult to rcommnd goods for targt usrs appropriatly (cold start). Howvr, xisting stor for rfrnc has nough purchas rcords to rcommnd goods to targt usrs. Thrfor, rsults rcall that is combination both xisting stor and nw stor ar dfinitly highr than thos rsults rcall that only adopt ir own purchas rcords a nw stor. Howvr, r wr fluctuations during xprimnt, as shown in Figur 2a. This is bcaus r ar uncrtaintis in onlin social ntworks, which may caus rcall rcommndation to hav fluctuations. Thn, rsults F -masur s two schms ar compard to comprhnsivly valuat prformanc proposd rcommndation schm. Th rsults F -masur ar shown in Figur 3a. Figur 3b shows comparison prcntag improvmnts F -masur in RM-MES schm. From rsults F -masur, it is clar that proposd mthod in our papr has a bttr prformanc than trust-basd schm at bginning. This is bcaus prcisions and rcalls proposd mthod in our papr ar highr than thos or mthods bcaus purchas rcords ar mor likly to b spars whn a nw stor opns. Thrfor, F -masur proposd mthod is highr than that or mthods. Thr is an incrasing numbr purchas

14 RM-MES schm. From rsults rcall, it is clar that RM-MES schm has bttr ffctivnss and prformanc than trust-basd schm at bginning. Whn a nw stor opns, historical purchas rcords is mor likly to b %; thus, it is difficult to rcommnd goods for targt usrs appropriatly (cold start). Howvr, xisting stor for rfrnc has nough purchas rcords to rcommnd goods to targt usrs. Thrfor, rsults rcall that is Information 29,, combination both xisting stor and nw stor ar dfinitly highr than thos rsults rcall that only adopt ir own purchas rcords a nw stor. Howvr, r wr fluctuations rcords during in xprimnt, nwly opnd as shown stor as in tim Figur passs. 2a. ThThis rsults is bcaus for Fr -masur ar uncrtaintis rcommndation in onlin modl social ntworks, will b maintaind which may at acaus constant rcall lvl. rcommndation to hav fluctuations. Figur 2. Th rsult xprimnt. (a) (a) Th Th rsults rsults rcall rcall in in a halth a halth and and prsonal prsonal car car shop; shop; (b) Information 29,, x FOR PEER REVIEW 4 8 (b) prcntag prcntag improvmnt rcall rcall in in halth halth and and prsonal prsonal car car shop. shop. Thn, rsults F-masur s two schms ar compard to comprhnsivly valuat prformanc proposd rcommndation schm. Th rsults F-masur ar shown in Figur 3a. Figur 3b shows comparison prcntag improvmnts F-masur in RM-MES schm. From rsults F-masur, it is clar that proposd mthod in our papr has a bttr prformanc than trust-basd schm at bginning. This is bcaus prcisions and rcalls proposd mthod in our papr ar highr than thos or mthods bcaus purchas rcords ar mor likly to b spars whn a nw stor opns. Thrfor, F-masur proposd mthod is highr than that or mthods. Thr is an incrasing numbr purchas rcords in nwly opnd stor as tim passs. Th rsults for F-masur rcommndation modl will b maintaind at a constant lvl. Th prcisions two schms ar closly compard undr diffrnt rcommndation thrsholds, Figur 3. as Th shown rsult in Figur xprimnt. 4a. From (a) Th rsults pictur abov, FF-masur w in in can a halth rach and prsonal conclusion car shop; that ffctivnss (b) prcntag proposd improvmnt mthod in FF-masur our papr in in is halth bttr and and than prsonal that car trust-basd shop. modl undr diffrnt rcommndation thrsholds α. This is bcaus trust-basd modl slcts ir own Th prcisions two schms ar closly compard undr diffrnt rcommndation purchas rcords to rcommnd. Howvr, purchas matrixs ar mor likly to b mpty at thrsholds, as shown in Figur 4a. From pictur abov, w can rach conclusion that bginning. Thus, it is difficult to rcommnd goods for targt usrs appropriatly. Whn ffctivnss proposd mthod in our papr is bttr than that trust-basd modl undr rcommndation thrshold is.5, rsults prcision in our proposd mthod ar highst and diffrnt rcommndation thrsholds α. This is bcaus trust-basd modl slcts ir own dcras ovr tim, and y rmain at zro whn thrshold is.9. purchas rcords to rcommnd. Howvr, purchas matrixs ar mor likly to b mpty at bginning. Thus, it is difficult to rcommnd goods for targt usrs appropriatly. Whn rcommndation thrshold is.5, rsults prcision in our proposd mthod ar highst and dcras ovr tim, and y rmain at zro whn thrshold is.9. A comparison prcntags improvmnts prcision undr diffrnt rcommndation thrsholds is shown in Figur 4b. As illustratd abov, it can b sn that prcntag prcision is furr improvd in RM-MES schm proposd in this papr. Th rcalls RM-MES schm proposd in this papr and trust-basd schm undr Figur 4. Th rsult xprimnt in halth and prsonal car shop. (a) Th rsults prcision diffrnt rcommndation thrsholds ar shown in Figur 5a. From Figur 5a, it can b sn that undr diffrnt rcommndation thrsholds; (b) prcntag improvmnt prcision undr rcall for RM-MES schm proposd in this papr is gratr than that trust-basd modl diffrnt rcommndation thrsholds. undr diffrnt rcommndation thrsholds. This is bcaus datasts RM-MES schm ar a combination A comparison historical prcntags purchas rcords improvmnts xisting prcision stor and undr diffrnt historical rcommndation purchas rcords thrsholds targt is stor. shown Whn in Figur rcommndation 4b. As illustratd thrshold abov, it α can is highr b sn than that.4, prcntag rsults rcall prcision in RM-MES is furr schm improvd gradually in RM-MES bcom smallr. schm Th proposd rcalls in this s papr. two rcommndation schms rmain at zro Th whn rcalls rcommndation RM-MES schm thrshold proposd is.9. in this papr and trust-basd schm undr diffrnt rcommndation thrsholds ar shown in Figur 5a. From Figur 5a, it can b sn that rcall for RM-MES schm proposd in this papr is gratr than that trust-basd modl undr diffrnt rcommndation thrsholds. This is bcaus datasts RM-MES schm ar a combination historical purchas rcords xisting stor and historical purchas rcords targt stor. Whn rcommndation thrshold α is highr than.4, rsults rcall in RM-MES schm gradually bcom smallr. Th rcalls s two rcommndation

15 Figur 3. Th rsult xprimnt. (a) Th rsults F-masur in a halth and prsonal car shop; 5 8 (b) prcntag improvmnt F-masur in halth and prsonal car shop. Information 29,, 8 Information 29,, x FOR PEER REVIEW 5 8 MES schm is mor stabl than trust-basd schms. A comparison prcntags improvmnts F-masur undr diffrnt rcommndation thrsholds is illustratd in Figur 6b. From rsults shown abov, it can b sn that prcntags F-masur ar furr improvd in RM-MES schm undr diffrnt thrsholds. Whn rcommndation thrshold is mor Figur4.4.Th Thrsult rsult xprimnt xprimntinin halth halthand andprsonal prsonalcar carshop. shop.(a) (a)th Thrsults rsultsprcision prcision Figur than undr.8, rcallrcommndation and prcision both (b) proposd schm in our paprand trust-basd diffrnt thrsholds; prcntag improvmnt prcision undr undr diffrnt rcommndation thrsholds; (b) prcntag improvmnt prcision undr schm ar zro; rfor, Fthrsholds. -masur rachs zro in xprimnt. diffrnt rcommndation diffrnt rcommndation thrsholds. A comparison prcntags improvmnts prcision undr diffrnt rcommndation thrsholds is shown in Figur 4b. As illustratd abov, it can b sn that prcntag prcision is furr improvd in RM-MES schm proposd in this papr. Th rcalls RM-MES schm proposd in this papr and trust-basd schm undr diffrnt rcommndation thrsholds ar shown in Figur 5a. From Figur 5a, it can b sn that rcall for RM-MES schm proposd in this papr is gratr than that trust-basd modl undr diffrnt rcommndation thrsholds. This is bcaus datasts RM-MES schm ar a combination historical purchas rcords xisting stor and historical purchas rcords targt stor. Whn rcommndation thrshold α is highr than.4, rsults rcall in RM-MES schm gradually bcom smallr. Th rcalls s two rcommndation schms rmain at zro whn rcommndation thrshold is.9. Figur Th Th rsult rsult xprimnt xprimnt in in halth halth and prsonal prsonal car car shop. shop. (a) (a) Th Th rsults rsults rcall rcall AFigur comparison prcnt improvmnts rcalland undr diffrnt rcommndation thrsholds is undr diffrnt diffrntrcommndation rcommndation thrsholds; (b) prcntag prcntag improvmnt rcall undr undr thrsholds; (b) improvmnt rcall undr diffrnt illustratd in Figur 5b. diffrnt rcommndation thrsholds. rcommndation thrsholds. To comprhnsivly valuat prformanc proposd schm, F-masur rsults s A two rcommndation schms wr compard diffrnt rcommndation rcommndation thrsholds thrsholds.is comparison prcnt improvmnts rcall undr undr diffrnt Th F-masurinFigur RM-MES schm proposd in this papr and trust-basd schm undr illustratd 5b. diffrnt rcommndation thrsholds illustratd in Figur 6a. It is clar that To comprhnsivly valuat isprformanc proposd schm, ffctivnss F-masur rsults Fs -masur in RM-MES schm is gratr than that trust-basd schm. Howvr, RM-MES two rcommndation schms wr compard undr diffrnt rcommndation thrsholds. schm combins historical purchas rcords rfrnc xisting stors and nwly opnd Th F-masur both RM-MES schm proposd in this papr and trust-basd schm undr stors togr. Thus, at bginning, it can rcommnd goods to usrs mor accuratly than diffrnt rcommndation thrsholds is illustratd in Figur 6a. It is clar that ffctivnss trust-basd schm. Whn rcommndation thrshold is.9, trust-basd rcall and prcision both a F-masur in RM-MES schm is gratr than that schm. in Howvr, trust-basd schm and RM-MES schm ar zro; rfor, F -masur s two RM-MES schm combins both historical purchas rcords rfrnc xisting stors and rcommndation schms ar both addition, it can b rcommnd sn from rsults blow thataccuratly RMnwly opnd stors togr. Thus,.atIn bginning, it can goods to usrs mor than trust-basd schm. Whn rcommndation thrshold is.9, rcall and prcision in both a trust-basd schm and RM-MES schm ar zro; rfor, F-masur s two Figur 6. Th rsult ar xprimnt halth and prsonal car shop. rsultsblow F-masur rcommndation schms both. in In addition, it can b sn from (a) Th rsults that undr diffrnt rcommndation thrsholds; (b) prcntag improvmnt F-masur undr RM-MES schm is mor stabl than trust-basd schms. A comparison prcntags diffrnt rcommndation thrsholds. improvmnts F-masur undr diffrnt rcommndation thrsholds is illustratd in Figur 6b. From rsults shown abov, it can b sn that prcntags F-masur ar furr improvd To furr valuat prformanc RM-MES schm, aftr xprimnt halth in RM-MES schm undr diffrnt thrsholds. Whn rcommndation thrshold is mor than and prsonal car catgory is complt, w carrid on xprimnt a nwly opnd baby.8, rcall and prcision both proposd schm in our papr and trust-basd schm ar products stor. In catgory baby products, r ar about 7,37 diffrnt kinds products. zro; rfor, F-masur rachs zro in xprimnt. In our xprimnt, w slctd most purchasd products as training st on basis sals rank. In xprimnts, w usd 3/4 slctd historical purchas rcords as training st and rst as tst st. Tabl 2 shows a summary xprimntal rsults. Tabl 2. Th xprimntal rsults prcision, rcall, and F-masur in baby products shop. Prcision Rcall F-masur TB.5.6. X =.3.4,37.39 X = X = X =.6.5..

16 Figur 5. Th rsult xprimnt in halth and prsonal car shop. (a) Th rsults rcall Information undr 29, diffrnt, 8 rcommndation thrsholds; (b) prcntag improvmnt rcall undr 6 8 diffrnt rcommndation thrsholds. Figur 6. Th rsult xprimnt in halth and prsonal car shop. (a) (a) Th Th rsults F F-masur undr diffrnt rcommndation thrsholds; (b) prcntag improvmnt F F-masur undr diffrnt rcommndation thrsholds. To To furr furr valuat valuat prformanc prformanc RM-MES RM-MES schm, schm, aftr aftr xprimnt xprimnt halth halth and and prsonal prsonal car car catgory catgory is complt, is complt, w carrid w carrid on on xprimnt xprimnt a nwly a opnd nwly baby opnd products baby stor. products In stor. catgory In catgory baby products, baby products, r ar r about ar 7,37 about diffrnt 7,37 diffrnt kinds kinds products. products. In our xprimnt, In our xprimnt, w slctd w slctd most purchasd most purchasd products products as training as st training on st basis on basis sals rank. In sals rank. xprimnts, In xprimnts, w usd 3/4 w usd slctd 3/4 historical slctd purchas historical rcords purchas as rcords training as st training and rst st and as tst rst st. as Tabl tst 2 st. shows Tabl a summary 2 shows a summary xprimntal xprimntal rsults. rsults. Tabl 2. Th xprimntal rsults prcision, rcall, and F -masur in baby products shop. Tabl 2. Th xprimntal rsults prcision, rcall, and F-masur in baby products shop. TB X =.3 X =.4 X =.5 X =.6 TB =.3 X =.4 X =.5 X =.6 Prcision Prcision Rcall.6, Rcall.6, F -masur F-masur From Tabl 2, w can rach conclusion that ffctivnss RM-MES schm is gratr From Tabl 2, w can rach conclusion that ffctivnss RM-MES schm is than that or schms. In addition, in proposd schm in our papr, w can obtain gratr than that or schms. In addition, in proposd schm in our papr, w can bst masurmnt rsults whn influnc factor transition probability x is.3. obtain bst masurmnt rsults whn influnc factor transition probability x is.3. At initiation stag, rsults prcision ratio, rcall ratio and F -masur ar improvd by approximatly 9.7%, 2.73%, and 2.2%, rspctivly, compard to prvious schms. 5. Conclusions In this papr, basd on trust-basd rcommndation modl, w proposd a nw rcommndation modl (RM-MES) basd on multi-motion similarity to improv prformanc rcommndation schm and ovrcom cold start problm. First, w dividd usrs bhaviors into browsing goods, buying goods, and purchasing goods as wll as valuating s goods. Thn, rcommndation attributs goods wr considrd to obtain similaritis btwn usrs and shops. Thn, most similar stor was slctd as rfrnc xisting stor in our xprimnt. Nxt, rcommndation probability matrix both xisting stor and nw stor wr calculatd according to similarity btwn usrs and targt usr. Finally, w adoptd Amazon product co-purchasing ntwork mtadata and commntary information to valuat ffctivnss and prformanc RM-MES schm through comprhnsiv xprimnts. Furrmor, w obtaind bst masurmnt rsults whn influnc factor transition probability x was.3 in our xprimnt. Thrfor, w compard dtaild information in RM-MES schm with that in trust-basd schm through xprimnts whn influnc factor transition probability x is.3 and analyzd impact transition probability influnc factors in RM-MES schm through xprimnts. Thrfor, w can draw conclusion that RM-MES schm has a bttr prformanc than or rcommndation schms.

17 Information 29,, For high probability goods in RM-MES schm, RM-MES schm will furr nhanc rcommndd probability goods that hav bn rcommndd and non-rcommndd goods will suffr furr rductions in ir rcommndation probabilitis. Thrfor, this tndncy lads to phnomnon that rcommndation systm loss opportunity to rcommnd mor optimizd goods. In futur studis, w will furr rsarch how to rcommnd or goods with small probabilitis to usrs to bring highr prit to systm. Author Contributions: Concptualization, J.L. and Y.W.; formal analysis, X.Y. and Y.W.; funding acquisition, J.L.; invstigation, Y.W.; mthodology, J.L. and Y.W.; projct administration, T.L. and Y.W..; stwar, T.L. and Y.W.; suprvision, X.Y.; validation, Q.L.; visualization, X.Y.; writing (original draft), Q.L. and Y.W.; writing (rviw and diting), Y.W. Funding: This rsarch was fundd by National Natural Scinc Foundation China (62725, 6379), and Fundamntal Rsarch Funds for Cntral Univrsitis Cntral South Univrsity (28zzts596). Acknowldgmnts: This work was supportd in part by National Natural Scinc Foundation China (647245, 64265, 67256, S652, M454), Ky Rsarch Program Hunan Provinc (26JC28), and Natural Scinc Foundation Hunan Provinc (28JJ299). Th authors would lik to thank rviwrs for ir valuabl suggstions and commnts. Conflicts Intrst: Th authors dclar no conflict intrst. Rfrncs. Wang, Y.; Yin, G.; Cai, Z.; Dong, Y.; Dong, H. A trust-basd probabilistic rcommndation modl for social ntworks. J. Ntw. Comput. Appl. 25, 55, [CrossRf] 2. L, H.; Kwon, J. Improvmnt matrix factorization-basd rcommndr systms using similar usr indx. Int. J. Stw. Eng. Appl. 25, 9, Joshi, B.; Iutzlr, F.; Amini, M.R. 6-Asynchronous Distributd Matrix Factorization with Similar Usr and Itm Basd Rgularization. In Procdings th ACM Confrnc on Rcommndr Systms-RcSys, Boston, MA, USA, 5 9 Sptmbr 26; pp Thorat, P.B.; Goudar, R.M.; Barv, S. Survy on Collaborativ Filtring, Contnt-basd Filtring and Hybrid Rcommndation Systm. Int. J. Comput. Appl. 25,, Polatidis, N.; Kaptanakis, S.; Pimnidis, E.; Kosmidis, K. Rproducibility xprimnts in rcommndr systms valuation. In Procdings IFIP Intrnational Confrnc on Artificial Intllignc Applications and Innovations, Rhods, Grc, 22 May 28; pp Zhu, J.; Zhang, J.; Zhang, C.; Wu, Q.; Jia, Y.; Zhou, B.; Yu, S. CHRS: Cold Start Rcommndation across Multipl Htrognous Information Ntworks. IEEE Accss 27, 99,. [CrossRf] 7. Alam, A.; Khusro, S.; Ullah, I.; Karim, M.S. Conflunc social ntwork, social qustion and answring community, and usr rputation modl for information sking and xprts gnration. J. Inf. Sci. 27, 43, [CrossRf] 8. Mao, X.; Mitra, S.; Swaminathan, V. Fatur Slction for FM-Basd Contxt-Awar Rcommndation Systms. IEEE Comput. Soc. 27, 42, Liu, X. An improvd clustring-basd collaborativ filtring rcommndation algorithm. Clustr Comput. 27, 2, Chn, T.; Hong, L.; Shi, Y.; Sun, Y. Joint Txt Embdding for Prsonalizd Contnt-basd Rcommndation. arxiv 27, arxiv: Mcauly, J.; Targtt, C.; Shi, Q.; Hngl, A.V.D. Imag-Basd Rcommndations on Styls and Substituts. In Procdings Intrnational Acm Sigir Confrnc on Rsarch and Dvlopmnt in Information Rtrival, Santiago, Chil, 9 3 August 25; pp Chn, M.; Wang, S.; Liang, P.P.; Baltrušaitis, T.; Zadh, A.; Morncy, L.P. Multimodal Sntimnt Analysis with Word-Lvl Fusion and Rinforcmnt Larning; ACM: Nw York, NY, USA, 27; pp Li, J.; Sun, C.; Lv, J. TCMF: Trust-Basd Contxt-Awar Matrix Factorization for Collaborativ Filtring. In Procdings IEEE Intrnational Confrnc on TOOLS with Artificial Intllignc, Limassol, Cyprus, 2 Novmbr 24; pp

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