Item Recommendation for Emerging Online Businesses

Transcription

1 Item Recommendation for Emerging Online Businesses Chun-Ta Lu Sihong Xie Weixiang Shao Lifang He Philip S. Yu University of Illinois at Chicago Presenter: Chun-Ta Lu

2 New Online Businesses Emerge Rapidly Emerging Business: launch year 1. startup business that is still in the developing stage 2. mature business that starts to enter new areas 2

3 New Online Businesses Emerge Rapidly Emerging Business: launch year 1. startup business that is still in the developing stage 2. mature business that starts to enter new areas [Zhang and Yu, 2015] Community detection for emerging networks. SDM,

4 Emerging Businesses Attract Limited Usage Online Business Share in China 3

5 Emerging Businesses Attract Limited Usage Recommenda)on for emerging businesses: Online Business Share in China Very sparse user data Lots of cold-start users w/ extremely few records 3

6 Opportunity: Users/Items appear in multiple websites Users are using multiple websites Same items appear in multiple websites 4

7 sport electronic P 1 P 2 P 3??? art C? 1 C2 C C E-commerce site 3 4 rate tag 5

8 Partially Aligned Networks: networks sharing common entities U 3 U 5 U 1 U 2 4 crowd-sourced review site U 4 I 1 I 2 I 3 4 aligned entity I 4 follow review sport electronic E-commerce site P 1 P 2 P 3??? art C? 1 C2 C C 3 4 rate tag En#ty alignment: [Zafarani and Liu, 2013] Connec#ng users across social media sites: a behavioral-modeling approach. KDD, [Lu et al., 2014] Iden#fying your customers in social networks. CIKM,

9 Partially Aligned Networks: networks sharing common entities U 3 U 5 U 1 U 2 4 crowd-sourced review site U 4 I 1 I 2 I 3 4 aligned entity I 4 follow review sport electronic E-commerce site P 1 P 2 P 3??? art C? 1 C2 C C 3 4 rate tag How to u)lize informa)on from par)ally aligned networks to improve performance? En#ty alignment: [Zafarani and Liu, 2013] Connec#ng users across social media sites: a behavioral-modeling approach. KDD, [Lu et al., 2014] Iden#fying your customers in social networks. CIKM,

10 Recommendation model: Collective Matrix Factorization (CMF) Y (t) P (t) Q (t)t Y is the user-item feedback matrix P 2 R n k and Q 2 R m k are low rank representations of users and items X p (t) i (i,g i )2A (t,s) p (s) g i 2 2 X q (t) j (j,g j )2A (t,s) q (s) g j 2 2 Y (s) P (s) Q (s)t Alignment Regularization: minimize the difference of the low rank representations of the same entities in source and target network 6

11 min P ( ),Q ( ) 0, 2{s,t} Recommendation model: Collective Matrix Factorization (CMF) J = X X 2{s,t} I ( ) (Y ( ) P ( ) Q ( )T ) 2 F + (R R A (P (t), P (s) )+R A (Q (t), Q (s) )) G R G R R + X 2 X ( P ( ) 2 F + Q ( ) 2 F ) 2{s,t} prevent overfitting Alignment Regularization: R A (P (t), P (s) )= 1 2 X p (t) i (i,g i )2A (t,s) p (s) g i 2 2 = 1 2 kaat P (t) AP (s) k 2 F 7

12 min P ( ),Q ( ) 0, 2{s,t} Recommendation model: Collective Matrix Factorization (CMF) J = X X 2{s,t} I ( ) (Y ( ) P ( ) Q ( )T ) 2 F + (R R A (P (t), P (s) )+R A (Q (t), Q (s) )) G R G R R + X 2 X ( P ( ) 2 F + Q ( ) 2 F ) 2{s,t} prevent overfitting Alignment Regularization: R A (P (t), P (s) )= 1 2 X p (t) i (i,g i )2A (t,s) p (s) g i 2 2 = 1 2 kaat P (t) AP (s) k 2 F Only few aligned en))es are available. Other non-aligned en))es can hardly benefit from it. Can we learn from similar users/items? 7

13 Preserve geometric closeness latent space original space Belkin et. al. "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples." JMLR,

14 Preserve geometric closeness latent space original space How to measure similarity between users/items with sparse informa)on? Belkin et. al. "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples." JMLR,

15 Similarity measures Similarity(, ) =? User data is very sparse in emerging business, these two users may not have anything in common. Homophily Principle two entities are considered to be similar if they are related to similar neighbors 9

16 Augmented Meta Path-based Similarity (AmpSim) Similarity(, ) =? Intra-Network AMP Inter-Network AMP Jack Richard Amy aligned 4!! (C [star]! P [star] C [star]! P [star] C) E-commerce site aligned 4 crowd-sourced review site!!!!! (C! U [score]! I [score] U! C), Both linkage structures and augmented link attributes are taken into account 10

17 Schema of Partially Aligned Networks Customer (C) Partially aligned User (U) Follow Star Tag (T) Rate Review Partially aligned Score Has Product (P) Item (I) U 1 U 4 2 crowd-sourced review site 5 U U 4 4 aligned entity U 5 I 1 I 2 I I 4 follow review e-commerce site G (t) crowd-sourced review site (network ) (network G (s) ) electronic sport P 1 P 2 P 3??? C art? 1 C2 C C E-commerce site 3 4 rate tag 11

18 Examples of AMP Intra-network augmented meta path (C [star]! P [star] C),(P [star] C [star]! P ), (P! T P ) (C [star]! P [star] C [star]! P [star] C) (P [star] C [star]! P [star] C [star]! P )!! Inter-network augmented meta path (P! I [score] U [score]! I! P ) (C! U [score]! I [score] U! C), (C! U! U! C) Different types of link attributes may appear in the same AMP. How to deal with it when measuring similarity? 12

19 Augmented Meta Path-based Similarity (AmpSim) Normalize link attributes to deal with different types of link attributes upon an AMP: M ui = Y ui b i q P j (Y uj b j ) 2 Y ui is the link attribute; P b i =is the bias term Ordinal attributes: b i = Y i Other attributes: b i =0 After normalization, MM T is adjusted cosine similarity if b i = Y i cosine similarity if b i =0 13

20 Augmented Meta Path-based Similarity (AmpSim) AmpSim: AMP-based similarity measure between v a based on path s(v a,v b P) = [Q l i=1 M i] ab +[ Q 1 P Q l i=1 M i tes upon P P i=l MT i ] ba [ Q l i=1 M i] a +[ Q 1 i=l MT i ] b 2 [0, 1] is the product of the normalized link attributes upon path direction is taken into account and v b P 14

21 Augmented Meta Path-based Similarity (AmpSim) AmpSim: AMP-based similarity measure between v a based on path s(v a,v b P) = [Q l i=1 M i] ab +[ Q 1 P Q l i=1 M i tes upon P P i=l MT i ] ba [ Q l i=1 M i] a +[ Q 1 i=l MT i ] b 2 [0, 1] is the product of the normalized link attributes upon path direction is taken into account and v b P Average over multiple AMPs: S(v a,v b )= tanh(p i w is i (v a,v b )) tanh(1) 2 [0, 1] on the AMP P i w i =1 14

22 Preserve geometric closeness latent space original space geometric regularization: X u,v S uv p u p v 2 2 = Tr(P T (D S)P) =Tr(P T L P P) Belkin et. al. "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples." JMLR,

23 Recommendation model: Amp-CMF min P ( ),Q ( ) 0, 2{s,t} J = X X X 2{s,t} I ( ) (Y ( ) P ( ) Q ( )T ) 2 F + (R A (P (t), P (s) )+R A (Q (t), Q (s) )) R R R + (RX G (P (t) )+R G (Q (t) )) R X G R G + X ( P ( ) 2 F + Q ( ) 2 F ) 2 2{s,t} CMF w/ alignement regularization geometric regularization prevent overfitting R A (P (t), P (s) )= 1 X p (t) i p (s) g 2 i 2 2 = 1 2 kaat P (t) AP (s) k 2 F R G (P) = 1 (i,g X i )2A (t,s) S uv p u p v 2 2 = Tr(PT (D S)P) = 1 2 Tr(PT L P P) u,v 16

24 Recommendation model: Amp-CMF min P ( ),Q ( ) 0, 2{s,t} J = X X X 2{s,t} I ( ) (Y ( ) P ( ) Q ( )T ) 2 F + (R A (P (t), P (s) )+R A (Q (t), Q (s) )) R R R + (RX G (P (t) )+R G (Q (t) )) R X G R G + X ( P ( ) 2 F + Q ( ) 2 F ) 2 using 2{s,t} mul)plica)ve updates CMF w/ alignement regularization geometric regularization prevent overfitting R A (P (t), P (s) )= 1 X p (t) i p (s) g 2 i 2 2 = 1 2 kaat P (t) AP (s) k 2 F R G (P) = 1 (i,g X i )2A (t,s) S uv p u p v 2 2 = Tr(PT (D S)P) = 1 2 Tr(PT L P P) u,v Solve the op)miza)on problem 16

25 Experiments - Datasets Name Table 1: Statistics of Datasets #users #items #tags #ratings #social links Yelp 26, 618 8, , , 765 Epinions 21, , , , 286 Yelp: test a mature business entering a new domain target network: Nevada w/ 11.5K users; 2.1K items source network: Arizona w/ 19.5K users; 6.3K items overlapping: 4.3K users; no items Epinions: test an emerging business partially Partial Split aligned with a developed business # of U target network (20% of ratings) source network (80% of ratings) overlapping (20% of users; all items) [Li and Lin, 2014] Matching users and items across domains to improve the recommendation quality. KDD,

26 Experiments - Evaluation Name Table 1: Statistics of Datasets #users #items #tags #ratings #social links Yelp 26, 618 8, , , 765 Epinions 21, , , , 286 Evaluation Metric: root-mean-square error (RMSE) and mean absolute error (MAE) Average results of 5-fold cross validation: 4 folds as training set and 1 fold as testing set cold start problem: test on users who have 5 ratings in training set about 90% and 55% of users are cold-start users in Yelp and Epinions, respectively 18

27 Experiments - Compared Methods WNMF + geometric regularization WNMF: weighted nonnegative matrix factorization Hete-MF: WNMF+ PathSim between items Hete-CF: WNMF + PathSim between users/items Hete-PRW: WNMF + Pairwise RW similarity between users/items Amp-MF: WNMF + AmpSim between users/items Transfer learning based methods CMF: collective matrix factorization w/ alignment regularization RMGM: rating-matrix generative model Amp-CMF: proposed method 19

28 Experiments - Yelp (RMSE) overall ± Method K = 10 K = 20 WNMF ± ± Hete-MF ± ± Hete-CF ± ± Hete-PRW ± ± Amp-MF ± ± CMF ± ± RMGM ± ± Amp-CMF ± ± ± cold start K = 10 K = ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

29 Experiments - Yelp (RMSE) overall ± Method K = 10 K = 20 WNMF ± ± Hete-MF ± ± Hete-CF ± ± Hete-PRW ± ± Amp-MF ± ± CMF ± ± RMGM ± ± Amp-CMF ± ± ± cold start K = 10 K = ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Methods using more accurate similarity measure achieve better performance 20

30 Experiments - Yelp (RMSE) overall ± Method K = 10 K = 20 WNMF ± ± Hete-MF ± ± Hete-CF ± ± Hete-PRW ± ± Amp-MF ± ± CMF ± ± RMGM ± ± Amp-CMF ± ± % improvement over WNMF ± cold start K = 10 K = ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Methods using more accurate similarity measure achieve better performance Combining both geometric and alignment regularization boosts the performance 20

31 Experiments - Yelp (MAE) overall ± Method K = 10 K = 20 WNMF ± ± Hete-MF ± ± Hete-CF ± ± Hete-PRW ± ± Amp-MF ± ± CMF ± ± RMGM ± ± Amp-CMF ± ± % improvement over WNMF cold start ± K = 10 K = ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Methods using more accurate similarity measure achieve better performance Combining both geometric and alignment regularization boosts the performance 21

32 Experiments - Epinion (RMSE) overall ± Method K = 10 K = 20 WNMF ± ± Hete-MF ± ± Hete-CF ± ± Hete-PRW ± ± Amp-MF ± ± CMF ± ± RMGM ± ± Amp-CMF ± ± % improvement over WNMF cold start an±std) on the cold-start Epinion d K = 10 K = ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

33 Experiments - Epinion (RMSE) overall ± Method K = 10 K = 20 WNMF ± ± Hete-MF ± ± Hete-CF ± ± Hete-PRW ± ± Amp-MF ± ± CMF ± ± RMGM ± ± Amp-CMF ± ± % improvement over WNMF cold start an±std) on the cold-start Epinion d K = 10 K = ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Learning from highly overlapped networks can achieve better improvement 22

34 Experiments - Parameter Sensitivity Yelp Epinions graph regularization alignment regularization Yelp Epinions 23

35 Experiments - Parameter Sensitivity Yelp Epinions graph regularization alignment regularization Yelp Epinions stable when the value is less than 1 23

36 Conclusion 1. AmpSim uses both the linkage structures and the augmented link attribute and achieves better performance 2. Combining both geometric and alignment regularization boosts the performance 3. Learning from highly overlapped networks can achieve better improvement (improve 21% in Yelp and 29% in Epinions) 24

37 Q&A Thank you! 25