A PERSONALITY-BASED AGGREGATION TECHNIQUE FOR GROUP RECOMMENDATION

Year 2020, Volume: 21 Issue: 4, 486 - 498, 28.12.2020

Emre Yalçın , Alper Bilge

https://doi.org/10.18038/estubtda.743422

Cited By: 1

Abstract

The main goal of a group recommender system is to provide appropriate referrals to a group of users sharing common interests rather than individuals. Such group referrals are commonly produced by utilizing aggregation techniques that analyze the propensities of the whole group by combining the preferences of the users in the group. Although there exist various aggregation techniques in the literature, they usually rely on the assumption that each member of the group has equal importance on the final decision of the group. However, the decision-making process of a group is a complicated process that is strongly correlated with not only group members' experience about the domain of interest but also their behavioral aspects; therefore, the influence of the individuals might be dependent on user personalities. In this study, we propose a personality-aware aggregation technique termed as the Personality weighted Average (PwAvg), which determines the influence degree of each member in the group using five fundamental personality traits, openness, agreeableness, emotional stability, conscientiousness, and extraversion; and then utilizes them to weight the preferences during the aggregation process. Experiments performed on two real-world benchmark datasets demonstrate that the PwAvg technique significantly outperforms three baseline aggregation techniques, especially for large user groups. Empirical outcomes also show that utilizing the PwAvg with emotional stability trait achieves more qualified group recommendations compared to others.

Keywords

Group recommender system, aggregation techniques, personality traits

References

• [1] Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 2005; 17(6): 734-749.
• [2] Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. Springer US, 2011.
• [3] Goren-Bar D, Glinansky O. FIT-recommending TV programs to family members. Computers & Graphics 2004; 28.2: 149-156.
• [4] McCarthy JF. Pocket restaurantfinder: A situated recommender system for groups. In: Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems, 2002.
• [5] Jameson A. More than the sum of its members: challenges for group recommender systems. In: Proceedings of the working conference on Advanced visual interfaces, 2004, 48-54.
• [6] McCarthy K, Salamó M, Coyle L, McGinty L, Smyth B, Nixon P. Cats: A synchronous approach to collaborative group recommendation. In: Florida Artificial Intelligence Research Society Conference (FLAIRS), 2006, 86-91.
• [7] Ardissono L, Goy A, Petrone G, Segnan M, Torasso P. Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Applied artificial intelligence 2003; 17(8-9): 687-714.
• [8] McCarthy JF, Anagnost TD. MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the 1998 ACM conference on computer supported cooperative work, 1998, 363-372.
• [9] Jameson A. More than the sum of its members: challenges for group recommender systems. In: Proceedings of the working conference on Advanced visual interfaces, 2004, 48-54.
• [10] Chao DL, Balthrop J, Forrest S. Adaptive radio: achieving consensus using negative preferences. In: Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work, 2005, 120-123.
• [11] Crossen A, Budzik J, Hammond KJ. Flytrap: intelligent group music recommendation. In: Proceedings of the 7th international conference on Intelligent user interfaces, 2002, 184-185.
• [12] Zhiwen Y, Xingshe Z, Daqing Z. An adaptive in-vehicle multimedia recommender for group users. In: IEEE 61st Vehicular technology conference, 2005, 5: 2800-2804.
• [13] O’connor M, Cosley D, Konstan JA, Riedl J. PolyLens: a recommender system for groups of users. In: ECSCW, 2001, 199-218.
• [14] Harper FM, Konstan JA. The movielens datasets: History and context. Acm transactions on interactive intelligent systems 2015, 5(4): 1-19.
• [15] Ntoutsi I, Stefanidis K, Norvag K, Kriegel HP. gRecs: A group recommendation system based on user clustering. In: International Conference on Database Systems for Advanced Applications, 2012, 299-303.
• [16] Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B. Happymovie: A facebook application for recommending movies to groups. In: 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011, 239-244.
• [17] Roy SB, Thirumuruganathan S, Amer-Yahia S, Das G, Yu C. Exploiting group recommendation functions for flexible preferences. In: IEEE 30th international conference on data engineering, 2014, 412-423.
• [18] Seo YD, Kim YG, Lee E, Seol KS, Baik DK. An enhanced aggregation method considering deviations for a group recommendation. Expert Systems with Applications 2018; 93: 299-312.
• [19] Boratto L, Carta S, Fenu G. Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering. Future Generation Computer Systems 2016; 64: 165-174.
• [20] Masthoff J. Group recommender systems: aggregation, satisfaction and group attributes. In: recommender systems handbook, 2015, 743-776.
• [21] Lieberman H, Van Dyke N, Vivacqua A. Let's browse: a collaborative browsing agent. Knowledge-Based Systems 1999; 12(8): 427-431.
• [22] Nunes MASN. Recommender systems based on personality traits, PhD, Université Montpellier, France, 2008.
• [23] Hu R, Pu P. A study on user perception of personality-based recommender systems. In: International conference on user modeling, adaptation, and personalization, 2010, 291-302.
• [24] Rossi S, Cervone F. Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study. In: ICAART (1), 2016, 38-46.
• [25] Costa Jr, Paul T, McCrae RR. Four ways five factors are basic. Personality and individual differences 1992; 13(6): 653-665.
• [26] Recio-Garcia JA, Jimenez-Diaz G, Sanchez-Ruiz AA, Diaz-Agudo B. Personality aware recommendations to groups. In: Proceedings of the third ACM conference on Recommender systems, 2009, 325-328.
• [27] Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B. Personality and social trust in group recommendations. In: 22Nd IEEE international conference on tools with artificial intelligence, 2010, (2): 121-126.
• [28] Boratto L, Carta S. State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Information retrieval and mining in distributed environments, 2010, 1-20.
• [29] Baltrunas L, Makcinskas T, Ricci F. Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems, 2010, 119-126.
• [30] Boratto L, Carta S. Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System. In: ICEIS, 2014, 564-572.
• [31] Khazaei E, Alimohammadi A. An automatic user grouping model for a group recommender system in location-based social networks. ISPRS International Journal of Geo-Information 2018; 7(2): 67.
• [32] Cantador I, Castells P. Extracting multilayered Communities of Interest from semantic user profiles: Application to group modeling and hybrid recommendations. Computers in Human Behavior 2011; 27(4): 1321-1336.
• [33] Schafer JB, Frankowski D, Herlocker J, Sen S. Collaborative filtering recommender systems. In: The adaptive web, 2007, 291-324.
• [34] Tkalcic M, Kunaver M, Tasic J, Košir A. Personality based user similarity measure for a collaborative recommender system. In: Proceedings of the 5th Workshop on Emotion in Human-Computer Interaction-Real world challenges, 2009, 30-37.
• [35] Nunes MAS, Hu R. Personality-based recommender systems: an overview. In: Proceedings of the sixth ACM conference on Recommender systems, 2012, 5-6.
• [36] Nguyen TT, Harper FM, Terveen L, Konstan JA. User personality and user satisfaction with recommender systems. Information Systems Frontiers, 2018; 20(6): 1173-1189.
• [37] Sacharidis D. Top-N group recommendations with fairness. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019, 1663-1670.