Web Movie Recommendation Using Reviews on the Web

Size: px

Start display at page:

Download "Web Movie Recommendation Using Reviews on the Web"

Darren Winfred Hopkins
5 years ago
Views:

1 SP1-E 2015 Web Web Movie Recommendation Using Reviews on the Web Takahiro Hayashi Rikio Onai Department of Information Engineering, Faculty of Engineering, Niigata University hayashi@ie.niigata-u.ac.jp Department of Informatics, Faculty of Informatics and Engineering onai@cs.uec.ac.jp keywords: recommendation systems, movie recommendation, user reviews Summary This paper proposes a movie recommendation system using movie reviews on the Web. The system receives a movie review from a user, estimates the user s interests on movies from the review, and provides other persons reviews to the user based on interest matching. This paper assumes that user s interests on movies appear on words which are positively or negatively evaluated in a review. Under the assumption, the system detects such words from the user s review, and choosesother persons reviews to recommend in which the detected words are positively evaluated. Experimental results have shown that more than 1/3 of recommended reviews can motivate users to watch the movies mentioned in the reviews. In addition, the results have indicated that combining the proposed recommendation method and a conventional TF-IDF based recommendation method is important for more efficient recommendation. 1. [Choi 12, Ding 05, Fleischman 03, Ghosh 99, 08, Roy 13, Said 10, Sarwar 01] () TF-IDF TF-IDF 1 2 1

2 Web 103 () 1 ( 1 ) 2 () 2 ( 2 ) TF-IDF ( ) 4 2 ( 1, 2) 5 6 1, [Choi 12, Ding 05, Ghosh 99, Said 10, Sarwar 01] 1 [Sinha 02] [Fleischman 03] [ 01] Park [Park 07] ( ) MineBlog [ 06] MineBlog Web 3 MineBlog

3 SP1-E 2015 TF-IDF 3. (TF-IDF ) 3 1 (4 ) TF-IDF TF-IDF ( ) ( DB ) (TF-IDF ) DB N T = {t 1,t 2,,t N } d v(d) v(d)=(w d t 1,w d t 2,,w d t N ) (1) wt d t d (TF-IDF ) w d t = tf n (t,d) idf(t) (2) tf n (d,t)= tf(d,t) n(d) (3) idf(t)=log M +1 (4) df (t) tf(t,d) d t n(d) d M (DB ) df (t) t tf(t,d) tf(t,d) n(d) tf n (t,d) TF-IDF 3 2 d d S 0 (d d) d d TF-IDF d d ( ) v(d) v(d ) S 0 (d d)= v(d) v(d ) v(d) v(d (5) ) 4. ( ) 1 2 ( 1, 2) 4 1 1( ) 1 d d v p (d) v p (d ) v p (d)=(wp d t 1,wp d t 2,,wp d t N ) (6) v p (d )=(wp d t 1,wp d t 2,,wp d t N ) (7) wp d t d t T { wp d wt d (if t T p (d)) t = (8) 0 (otherwise) T p (d) d ( ) TF-IDF TF-IDF 0 1 d d S 1 (d d) S 1 (d d)= v p(d) v p (d ) v p (d) v p (d (9) ) ( ) 2 d v n (d) d (7) v p (d ) v n (d)=(wn d t 1,wn d t 2,,wn d t N ) (10)

Web 105 wn d t d t T { wn d wt d (if t T n (d)) t = (11) 0 (otherwise) T n (d) d ( ) 4 3 2 TF- IDF TF-IDF 0 1 2 d d S 2 (d d) S 2 (d d)= v

4 Web 105 wn d t d t T { wn d wt d (if t T n (d)) t = (11) 0 (otherwise) T n (d) d ( ) TF- IDF TF-IDF d d S 2 (d d) S 2 (d d)= v n(d) v p (d ) v n (d) v p (d (12) ) , 2 [ 04] 1 (1) (2) MeCab[Kubo 04] CaboCha[ 02] (3) [ 05] [ 08] 1 [ 04] 1 2 (4)

106 30 1 SP1-E 2015 2 Web HTML 5 2 Web 2 HTML MySQL 3 5 3 Web Web Web 3 3(a) 3(c) 3(b) 1, 2 2 ( ) 5 4 1, 2 (1) (2) TF-IDF (3) 1 (4) 2 3 (http://www.jtnews.

5 SP1-E Web HTML 5 2 Web 2 HTML MySQL Web Web Web 3 3(a) 3(c) 3(b) 1, 2 2 ( ) 5 4 1, 2 (1) (2) TF-IDF (3) 1 (4) 2 3 ( MySQL( 3 (5) TF-IDF (6) S 1 ( (9)) S 2 (12)) (7) , , 2 3 TF-IDF Web 20, ,

Web 107 1 ( ( ) ( A (1) (2) ) ) [%] 95% [%] 26(67/257) [21,31] 32(62/191) [26,39] 1 38(87/232) [31,44] 2 36(84/233) [30,42] 4 A (1) (2) (3) (4) (5) A (1)(2) 2 95% 1 32% 3 1 6% 95% 1 38% 2 36% 1 12% 2

6 Web ( ( ) ( A (1) (2) ) ) [%] 95% [%] 26(67/257) [21,31] 32(62/191) [26,39] 1 38(87/232) [31,44] 2 36(84/233) [30,42] 4 A (1) (2) (3) (4) (5) A (1)(2) 2 95% 1 32% 3 1 6% 95% 1 38% 2 36% 1 12% 2 10% 95% 1 6% 2 4%95% 1 2 α =5% Welch t ( ) 1, 2 (p p =0.01,p=0.01) (p p =0.15,p=0.22) (p =0.08) %(236/300) 2 81%(244/300) 91%(272/300) 1 38%(73/192) 2 36%(71/195) 33%(58/175) ( ( ) ( A (1) (2) ) )

108 30 1 SP1-E 2015 1, 2 (5 ) 1 2 1 2 1 2 (1) (2) (1) 1 2 (2) 1 2 1 2 6 2 2 1 2 1 2 1 1 2 1 1 A B A B 5 1 A (1)(2) B 2 5 B ( ( A (1) (2) ) ( ) ) 95% 1 C 1 47% (120/253) [42, 51] C 2 41% (176/434)

7 SP1-E , 2 (5 ) (1) (2) (1) 1 2 (2) A B A B 5 1 A (1)(2) B 2 5 B ( ( A (1) (2) ) ( ) ) 95% 1 C 1 47% (120/253) [42, 51] C 2 41% (176/434) [37, 47] C 5 38% (333/872) [30, 42] 2 C 3 45% (95/212) [41, 50] C 4 39% (136/348) [34, 46] C 5 38% (333/872) [30, 42] C 1 C 2 ( ) C 3 C 4 ( ) C 5 1 C 1 A (1)(2) C 1 C 2 C C 3 C 4 C , C 1

8 Web % C 2 41% C 5 38% C 1 C 1 C 2 C 5 5% Welch t ( ) C 1 C 3 C 5 ( p =0.03,p=0.01) C 3 45% C 4 39% C 5 38% C 3 C 3 C 4 C 5 α =5% Welch t ( ) C 3 C 5 (p =0.02) C 4 (p =0.08) α = 10% ! ( ) 1, ( 3 5 ) 1, 2 OR 1, , d v u (d)=(wu d t 1,wu d t 2,,wu d t N ) wu d t d t T { w wu d t = d t (if t T p (d) T n (d)) 0 (otherwise) T p (d) T n (d) d 3 d d S 3 (d d ) S 3 (d d)= v u(d) v p (d ) v u (d) v p (d 3 )

110 30 1 SP1-E 2015 3 ( ( ) ( A (1) (2) ) ) [%] 95% [%] 29(77/266) [22, 36] 32(67/209) [27, 37] 3 38(88/232) [35, 43] 6 1, 2, 3 3 1 1 10 30 30 1, 2, 3 300 ( 1 10 ) 2 1, 2, 3 6 3

9 SP1-E ( ( ) ( A (1) (2) ) ) [%] 95% [%] 29(77/266) [22, 36] 32(67/209) [27, 37] 3 38(88/232) [35, 43] 6 1, 2, , 2, ( 1 10 ) 2 1, 2, %(250/300) 1 2 1, 2 3 1, ( ) 3 () ( 3 ) , 2 ( 1) 3 1, % α =5% Weltch t ( ) 3 (p =0.031) (p =0.055) ( 3 1 ) 7. 2 (

Web 111 1 2) 3 1 2 2 3 3 3 ( ) ( ) [Choi 12] S. Choi, S. Ko, and Y. Han: A Movie Recommendation Algorithm Based on Genre Correlations, Expert Systems with Applications, Vol. 39, No. 9, pp.

485 492 (2005) [Fleischman 03] M. Fleischman, and E.

10 Web ) ( ) ( ) [Choi 12] S. Choi, S. Ko, and Y. Han: A Movie Recommendation Algorithm Based on Genre Correlations, Expert Systems with Applications, Vol. 39, No. 9, pp (2012) [Ding 05] Y. Ding and X. Li: Time Weight Collaborative Filtering, Proceedings of ACM International Conerence on Information and Knowledge Management, pp (2005) [Fleischman 03] M. Fleischman, and E. Hovy: Recommendations without User Preferences: A Natural Language Processing Approach, Proceedings of International Conference on Intelligent User Interfaces, pp (2003) [Ghosh 99] S. Ghosh, M. Mundhe, K. Hernandez. and S. Sen: Voting for Movies: the Anatomy of a Recommender System, Proceedings of Annual Conference on Autonomous Agents, pp (1999) [ 05] Vol. 12, No. 2, pp (2005) [ 01] ZASH : Vol. 42, No. 8, pp (2001) [Kubo 04] T. Kudo, K. Yamamoto. and Y. Matsumoto: Applying Conditional Random Fields to Japanese Morphological Analysis, Proceedings of Conference on Empirical Methods in Natural Language Processing, pp (2004) [ 02] Vol. 43 No. 6 pp , [ 06] MineBlog: blog Vol. 47, No. 4, pp (2006) [ 08] Vol. 49, No. 1, pp (2008) [Park 07] S.T. Park and D.M. Pennock: Applying Collaborative Filtering Techniques to Movie Search for Better Ranking and Browsing, Proceedings ofacm International Conference on Knowledge Discovery and Data Mining, pp (2007) [Roy 13] D. Roy and A. Kundu: Design of Movie Recommendation System by Means of Collaborative Filtering, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 4, pp (2013) [Said 10] A. Said, S. Berkovsky and E.W. Luca: Putting Things in Context: Challenge on Context-Aware Movie Recommendation, Proceedings of the Workshop on Context-Aware Movie Recommendation, pp. 2-6 (2010) [Sarwar 01] B. Sarwar, G. Karypis, J. Konstan and J. Riedl: Item- Based Collaborative Filtering Recommendation Algorithms, Proceedings of International Conference on World Wide Web, pp (2001) [Sinha 02] R. Sinha and K. Swearingen: The Role of Transparency in Recommender Systems, Proceedings of Conference on Human Factors in Computing Systems, pp (2002) [ 04],, Weblog ( ) SIG-SWO-A (2004) [ 08],, 14 pp (2008) ( ) IEEE ( NTT) ICOT RWC 2000 ( ) ACM

Web. Web. Web Google *1. Web [1] SBM SBM. iphone [2][3] Web. Web Web. Web. Web

Web. Web. Web Google *1. Web [1] SBM SBM. iphone [2][3] Web. Web Web. Web. Web Web 1 1 1 1 Web 0.81 1. Web Google *1 Yahoo *2 Web [1] iphone [2][3] Web Web Web Web 1 *1 https://www.google.co.jp/ *2 http://www.yahoo.co.jp/ Web ( ) 2 Web Web Web ( SBM ) SBM Web Web 2. Web SBM Web SBM