A group recommender for movies based on content similarity and popularity

Information Processing and Management

Volumn 49, Issue 3, 2013, Pages 673-687

  Pera, Maria S ^a   Ng, Yiu Kai ^a  

^a 3361 TMCB   (United States)

Author keywords

Content similarity; Group recommender; Movie; Popularity

Indexed keywords

COLLABORATIVE FILTERING;

CONTENT SIMILARITY; DOMAIN INDEPENDENTS; EMPIRICAL STUDIES; GROUP RECOMMENDATIONS; GROUP RECOMMENDERS; MOVIE; MOVIE RECOMMENDATIONS; POPULARITY;

MOTION PICTURES;

EID: 84878109389     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2012.07.007     Document Type: Article

References (44)

1. Adomavicius, G., & Kwon, Y. (2012). Improving aggregate recommendation diversity using ranking-based techniques. IEEE TKDE, 24(5), 896-911
2. Amer-Yahia, S., Basu Roy, S., Chawlat, A., Das, G., & Yu, C. (2009). Group recommendation: Semantics and efficiency. Very Large Data Base Endowment, 2(1), 754-765
3. Baltrunas, L., Makcinskas, T. & Ricci, F. (2010). Group recommendations with rank aggregation and collaborative filtering. In ACM RecSys (pp. 119-126)
4. Banerjee, S. & Pedersen, T. (2003). Extended gloss overlaps as a measure of semantic relatedness. In IJCAI (pp. 205-810)
5. Basu Roy, S., Amer-Yahia, S., Chawla, A., Das, G., & Yu, C. (2010). Space efficiency in group recommendation. Very Large Data Base Journal, 19(6), 877-900
6. Bellogin, A., Cantador, I. & Castells, P. (2010). A study of heterogeneity in recommendations for a social music service. In ACM HetRec (pp. 1-8)
7. Berkovsky, S. & Freyne, J. (2010). Group-based recipe recommendations: Analysis of data aggregation strategies. In ACM RecSys (pp. 111-118)
8. Berkovsky, S., Freyne, J. & Coombe, M. (2009). Aggregation trade offs in family based recommendations. In AI (pp. 646-655)
9. Boratto, L., & Carta, S. (2011). State-of-the-art in group recommendation and new approaches for automatic identification of groups. Studies in Computational Intelligence, 324, 1-20
10. Boratto, L., Carta, S., Chessa, A., Agelli, M. & Clemente, M. (2009). Group recommendation with automatic identification of users communities. In IEEE/WIC/ACM WI-IAT (pp. 547-550)
11. Cantador, I., & Castells, P. (2011). Extracting multilayered communities of interest from semantic user profiles: Application to group modeling and hybrid recommendations. Computers in Human Behavior, 27(4), 1321-1336
12. Cantador, I., Bellogin, A., & Vallet, D. (2010). Content-based recommendation in social tagging systems. In ACM RecSys (pp. 237-240)
13. Christen, P. (2008). Automatic record linkage using seeded nearest neighbor and support vector machine classification. In ACM SIGKDD (pp. 151-159)
14. Christensen, I., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert Systems with Applications, 38, 14127-14135
15. Cormack, G., Clarke, C. & Buettcher, S. (2009). Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In ACM SIGIR (pp. 758-759)
16. Croft, W., Metzler, D., & Strohman, T. (2010). Search engines: Information retrieval in practice. Addison Wesley
17. Crossen, A., Budzik, J. & Hammond, K. (2002). Flytrap: Intelligent group music recommendation. In ACM IUI (pp. 184-185)
18. Deshpande, M., & Karypis, G. (2004). Item-based top-N recommendation algorithms. ACM Transactions on Information Systems, 22(1), 143-177
19. Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., et al. (2010). Enhancing group recommendation by incorporating social relationship interactions. In ACM GROUP (pp. 97-106)
20. GroupLens Research at University of Minnesota (2011). http://www.grouplens.org/system/files/hetrec2011-movielens-2k.zip
21. Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., et al. (2010). Document recommendation in social tagging services. In WWW (pp. 391-400)
22. Gustafson, N. & Ng, Y.-K. (2008). Augmenting data retrieval with information retrieval techniques by using word similarity. In NLDB (pp. 163-174)
23. Guy, I., Zwerdling, N., Ronen, I., Carmel, D. & Uziel, E. (2010). Social media recommendation based on people and tags. In ACM SIGIR (pp. 194-201)
24. Herlocker, J., Konstan, J., Borchers, A. & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In ACM SIGIR (pp. 230-237)
25. Jameson, A., & Smyth, B. (2007). Recommendation to groups. In Brusilovsky, P., Kobsa, A. & Nejdl, W. (Eds.), The adaptive web: Methods and strategies of web personalization (pp. 596-627)
26. Koberstein, J., & Ng, Y.-K. (2006). Using word clusters to detect similar web documents. In KSEM (pp. 215-228).
27. Leacock, C., & Chodorow, M. (1998). Combining local context and wordnet similarity for word sense identification. The MIT Press.
28. Lee, J. (1997). Analyses of multiple evidence combination. In ACM SIGIR (pp. 267-276)
29. Manning, C., & Schutze, H. (2003). Foundations of statistical natural language processing. The MIT Press
30. Masthoff, J. (2004). Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and User-Adapted Interaction, 14(1), 37-85
31. Masthoff, J., & Gatt, A. (2006). In pursuit of satisfaction and the prevention of embarrassment: Affective state in group recommender systems. User Modeling User-Adapted Interaction, 16(3-4), 281-319
32. McCarthy, J. & Anagnost, T. (1998). MusicFX: An arbiter of group preferences for computed supported collaborative workouts. In CSCW (pp. 363-372)
33. McCarthy, K., Salamo, M., Coyle, L., McGinty, L., Smyth, B., & Nixon, P. (2006). Group recommender systems: A critiquing based approach. In ACM IUI (pp. 267-269)
34. Ochi, P., Rao, S., Takayama, L., & Nass, C. (2010). Predictors of user perceptions of web recommender systems: How the basis for generating experience and search product recommendations affects user responses. International Journal of Human-Computer Studies, 68, 472-482
35. O'Connor, M., Cosley, D., Konstan, J., & Riedl, J. (2001). PolyLens: A recommender system for groups of users. In ECSCW (pp. 199-218)
36. Park, M., Park, H., & Cho, S. (2008). Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making. In APCHI (pp. 114-122)
37. Pera, M.S. & Ng, Y.-K. (2011). With a little help from my friends: Generating personalized book recommendations using data extracted from a social website. In IEEE/WIC/ACM WI-IAT (pp. 96-99)
38. Pera, M. S., Lund, W., & Ng, Y.-K. (2009). A sophisticated library search strategy using folksonomies and similarity matching. Journal of the American Society for Information Science and Technology, 60(7), 1406-1932
39. Pera, M. S., & Ng, Y.-K. (2010). A Naive Bayes classifier for web document summaries created by using word similarity and significant factors. International Journal of Advanced Information Technology, 19(4), 465-486
40. Qumsiyeh, R., Pera, M. S., & Ng, Y.-K. (2011). Generating exact-and ranked partially-matched answers to questions in advertisements. Proceedings of the VLDB Endowment, 5(3), 217-228
41. Recio-Garcia, J., Jimenez-Diaz, G., Sanchez-Ruiz, A., & Diaz-Agudo, B. (2009). Personality aware recommendations to groups. In ACM RecSys (pp. 325-328)
42. Shavitt, Y., Weinsberg, E., & Weinsberg, U. (2010). Building recommendation systems using peer-to-peer shared content. In ACM CIKM (pp. 1457-1460)
43. Yu, C., Lakshmanan, L., & Amer-Yahia, S. (2009). It takes variety to make a world: Diversification in recommender systems. In EDBT (pp. 368-378)
44. Zheng, H., Wang, D., Zhang, Q., Li, H., & Yang, T. (2010). Do clicks measure recommendation relevancy? An empirical user study. In ACM RecSys (pp. 249-252).