Enhancing group recommendation by incorporating social relationship interactions

Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  Barry Smyth,et al.  Recommendation to Groups , 2007, The Adaptive Web.

[3]  Luis M. de Campos,et al.  Managing uncertainty in group recommending processes , 2009, User Modeling and User-Adapted Interaction.

[4]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[5]  Jennifer Neville,et al.  Modeling relationship strength in online social networks , 2010, WWW '10.

[6]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[7]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[8]  Judith Masthoff,et al.  Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers , 2004, User Modeling and User-Adapted Interaction.

[9]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[10]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[11]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[12]  Xingshe Zhou,et al.  Handling conditional preferences in recommender systems , 2009, IUI.

[13]  Hugo Liu.  InterestMap : Harvesting Social Network Profiles for Recommendations , 2004 .

[14]  John Riedl,et al.  PolyLens: A recommender system for groups of user , 2001, ECSCW.

[15]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[16]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[17]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[18]  Shlomo Berkovsky,et al.  Aggregation Trade Offs in Family Based Recommendations , 2009, Australasian Conference on Artificial Intelligence.

[19]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[20]  Shivakant Mishra,et al.  Fusing mobile, sensor, and social data to fully enable context-aware computing , 2010, HotMobile '10.

[21]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[22]  Xingshe Zhou,et al.  TV Program Recommendation for Multiple Viewers Based on user Profile Merging , 2006, User Modeling and User-Adapted Interaction.

[23]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[24]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[25]  Yehuda Koren,et al.  The BellKor Solution to the Netflix Grand Prize , 2009 .

[26]  Cong Yu,et al.  Group Recommendation: Semantics and Efficiency , 2009, Proc. VLDB Endow..

[27]  Alessandro Chessa,et al.  Group Recommendation with Automatic Identification of Users Communities , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[28]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[29]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[30]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[31]  Melanie Tory,et al.  Music selection using the PartyVote democratic jukebox , 2008, AVI '08.

[32]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[33]  Petteri Alahuhta,et al.  Unobtrusive dynamic modelling of TV programme preferences in a Finnish household , 2009, Multimedia Systems.