Abstract
In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for individual users, in many circumstances the selected items (e.g., movies) are not intended for personal usage but rather for consumption in groups. This paper investigates how effective group recommendations for movies can be generated by combining the group members’ preferences (as expressed by ratings) or by combining the group members’ recommendations. These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group recommendations for different group compositions. The group recommendations are not only assessed in terms of accuracy, but also in terms of other qualitative aspects that are important for users such as diversity, coverage, and serendipity. In addition, the paper discusses the influence of the size and composition of the group on the quality of the recommendations. The results show that the grouping strategy which produces the most accurate results depends on the algorithm that is used for generating individual recommendations. Therefore, the paper proposes a combination of grouping strategies which outperforms each individual strategy in terms of accuracy. Besides, the results show that the accuracy of the group recommendations increases as the similarity between members of the group increases. Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm. Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations. The conclusions of this paper can be used as guidelines for this selection process.
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Ardissono L, Goy A, Petrone G, Segnan M, Torasso P (2002) Tailoring the recommendation of tourist information to heterogeneous user groups. In: Reich S, Tzagarakis M, De Bra P (eds) Hypermedia: openness, structural awareness, and adaptivity. Lecture notes in computer science, vol 2266. Springer, Berlin/Heidelberg, pp 228–231
Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the 4th ACM conference on Recommender Systems, RecSys ’10. ACM, New York, pp 119–126
Berkovsky S, Freyne J (2010) Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the fourth ACM conference on recommender systems, RecSys ’10. ACM, New York, pp 111–118. doi:10.1145/1864708.1864732
Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on Uncertainty in Artificial Intelligence, UAI’98. San Francisco, CA, pp 43–52. http://dl.acm.org/citation.cfm?id=2074094.2074100
Chao DL, Balthrop J, Forrest S (2005) Adaptive radio: achieving consensus using negative preferences. In: Proceedings of the 2005 international ACM SIGGROUP conference on supporting group work, GROUP ’05. ACM, New York, pp 120–123. doi:10.1145/1099203.1099224
Chen YL, Cheng LC, Chuang CN (2008) A group recommendation system with consideration of interactions among group members. Expert Syst Appl 34(3):2082–2090. doi:10.1016/j.eswa.2007.02.008. http://www.sciencedirect.com/science/article/pii/S0957417407000863
Crossen A, Budzik J, Hammond KJ (2002) Flytrap: intelligent group music recommendation. In: Proceedings of the 7th international conference on Intelligent User Interfaces, IUI ’02. ACM, New York, NY, pp 184–185
Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22(1):143–177. doi:10.1145/963770.963776
Dooms S, De Pessemier T, Martens L (2011) A user-centric evaluation of recommender algorithms for an event recommendation system. In: Proceedings of the workshop on user-centric evaluation of recommender systems and their interfaces at ACM conference on Recommender Systems (RECSYS), pp 67–73
Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the fourth ACM conference on recommender systems, RecSys ’10. ACM, New York, NY, pp 257–260. doi:10.1145/1864708.1864761
Goren-Bar D, Glinansky O (2002) Family stereotyping—a model to filter tv programs for multiple viewers. In: Proceedings of the 2nd workshop on personalization in future tv
Grouplens Research (2011) MovieLens data sets. http://www.grouplens.org/node/73. Accessed 13 July 2012
Herlocker JL, Konstan JA, Riedl J (2000) Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on computer supported cooperative work, CSCW ’00. ACM, New York, pp 241–250
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53. doi:10.1145/963770.963772
Jameson A (2004) More than the sum of its members: challenges for group recommender systems. In: Proceedings of the working conference on Advanced Visual Interfaces, AVI ’04. ACM, New York, pp 48–54
Jameson A, Baldes S, Kleinbauer T (2004) Two methods for enhancing mutual awareness in a group recommender system. In: Proceedings of the working conference on Advanced Visual Interfaces, AVI ’04. ACM, New York, NY, pp 447–449
Jameson A, Smyth B (2007) The adaptive web. chap. Recommendation to groups, pp 596–627. Springer-Verlag, Berlin, Heidelberg. http://dl.acm.org/citation.cfm?id=1768197.1768221
Kay J, Niu W (2006) Adapting information delivery to groups of people. In: Proceedings of the workshop on new technologies for personalized information access at the 10th international conference on user modeling
Lieberman H, van Dyke N, Vivacqua A (1999) Let’s browse: a collaborative browsing agent. Knowl-Based Syst 12(8):427–431. doi:10.1016/S0950-7051(99)00036-2. http://www.sciencedirect.com/science/article/pii/S0950705199000362
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge Univ. Press, New York, NY
Masthoff J (2004) Group modeling: selecting a sequence of television items to suit a group of viewers. User Model User-Adap Inter 14:37–85
McCarthy J (2002) Pocket restaurantfinder: a situated recommender system for groups. In: Proceedings of the workshop on mobile AdHoc communication at the 2002 ACM conference on human factors in computer systems. ACM
McCarthy JF, Anagnost TD (1998) Musicfx: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the 1998 ACM conference on Computer Supported Cooperative Work, CSCW ’98. ACM, New York, NY, pp 363–372
McCarthy K, Salamo M, Coyle L, McGinty L, Smyth B, Nixon P (2006) Cats: a synchronous approach to collaborative group recommendation. In: Sutcliffe G, Goebel R (eds) FLAIRS conference. AAAI Press, pp 86–91
McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI ’06 extended abstracts on human factors in computing systems, CHI EA ’06. ACM, New York, pp 1097–1101. doi:10.1145/1125451.1125659
Murakami T, Mori K, Orihara R (2008) Metrics for evaluating the serendipity of recommendation lists. In: Satoh K, Inokuchi A, Nagao K, Kawamura T (eds) New frontiers in artificial intelligence. Lecture notes in computer science, vol 4914. Springer, Berlin/Heidelberg, pp 40–46
O’Connor M, Cosley D, Konstan JA, Riedl J (2001) Polylens: a recommender system for groups of users. In: Proceedings of the seventh conference on European conference on computer supported cooperative work, ECSCW’ 01. Norwell, MA, pp 199–218. http://dl.acm.org/citation.cfm?id=1241867.1241878
Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B (2010) Personality and social trust in group recommendations. In: Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI ’10, vol 02. IEEE Computer Society, Washington, DC, pp 121–126. doi:10.1109/ICTAI.2010.92
Ricci F, Rokach L, Shapira B, Kantor PB (2010) Recommender systems handbook, 1st edn. Springer-Verlag New York, Inc., New York, NY
Smyth B, Balfe E, Freyne J, Briggs P, Coyle M, Boydell O (2004) Exploiting query repetition and regularity in an adaptive community-based web search engine. User Model User-Adap Inter 14:383–423. doi:10.1007/s11257-004-5270-4
Telematica Instituut/Novay (2009) Duine framework. http://duineframework.org/. Accessed 13 July 2012
The Apache Software Foundation (2012) Apache Mahout. http://mahout.apache.org/. Accessed 13 July 2012
Yu Z, Zhou X, Hao Y, Gu J (2006) Tv program recommendation for multiple viewers based on user profile merging. User Model User-Adap Inter 16:63–82. http://dl.acm.org/citation.cfm?id=1146521.1146531
Zhiwen Y, Xingshe Z, Daqing Z (2005) An adaptive in-vehicle multimedia recommender for group users. In: 2005 IEEE 61st Vehicular technology conference, 2005. VTC 2005-Spring, vol 5, pp 2800–2804
Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web, WWW ’05. ACM, New York, NY, pp 22–32. doi:10.1145/1060745.1060754
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De Pessemier, T., Dooms, S. & Martens, L. Comparison of group recommendation algorithms. Multimed Tools Appl 72, 2497–2541 (2014). https://doi.org/10.1007/s11042-013-1563-0
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DOI: https://doi.org/10.1007/s11042-013-1563-0