Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation

Frequent group activities of human beings have become an indispensable part in their daily life. Group recommendation can recommend satisfactory activities to group members in the recommender systems, and the key issue is how to aggregate preferences in different group members. Most existing group recommendation employed the predefined static aggregation strategies to aggregate the preferences of different group members, but these static strategies cannot simulate the dynamic group decision-making. Meanwhile, most of these methods depend on intuitions or assumptions to analyze the influence of group members and lack of convincing theoretical support. We argue that the influence of group members plays a particularly important role in group decision-making and it can better assist group profile modeling and perform more accurate group recommendation. To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation, namely SIAGR (short for "Social Influence-based Attentive Group Recommendation"), which takes attention mechanisms and the popular method (BERT) as the aggregation representation for group profile modeling. Specifically, we analyze the influence of group members based on social identity theory and two-step flow theory and exploit an attentive mavens mining method. In addition, we develop a BERT-based representation method to learn the interaction of group members. Lastly, we complete the group recommendation under the neural collaborative filtering framework and verify the effectiveness of the proposed method by experimenting.

[1]  Chao Yang,et al.  Attentive Group Recommendation , 2018, SIGIR.

[2]  Tat-Seng Chua,et al.  Learning Image and User Features for Recommendation in Social Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[3]  Wen Gao,et al.  Cross-media analysis and reasoning: advances and directions , 2017, Frontiers of Information Technology & Electronic Engineering.

[4]  Joseph F. McCarthy,et al.  MusicFX: an arbiter of group preferences for computer supported collaborative workouts , 1998, CSCW '98.

[5]  Judith Masthoff,et al.  Group Recommender Systems: Combining Individual Models , 2011, Recommender Systems Handbook.

[6]  Stephen J. O'Brien.  Cats , 2004, Current Biology.

[7]  Lin Wu,et al.  Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval , 2017, IEEE Transactions on Image Processing.

[8]  Ludovico Boratto,et al.  State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups , 2011, Information Retrieval and Mining in Distributed Environments.

[9]  Yukihiro Tagami,et al.  Embedding-based News Recommendation for Millions of Users , 2017, KDD.

[10]  Julian J. McAuley,et al.  Self-Attentive Sequential Recommendation , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[11]  Lin Wu,et al.  Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Zhang,et al.  Social Identity Theory and It’s Development , 2006 .

[13]  Jonathan L. Herlocker,et al.  A collaborative filtering algorithm and evaluation metric that accurately model the user experience , 2004, SIGIR '04.

[14]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[15]  Lin Wu,et al.  Iterative Views Agreement: An Iterative Low-Rank Based Structured Optimization Method to Multi-View Spectral Clustering , 2016, IJCAI.

[16]  Xu Chen,et al.  Aesthetic-based Clothing Recommendation , 2018 .

[17]  Aditya Khamparia,et al.  Online Social Network Analysis , 2019, Advances in Social Networking and Online Communities.

[18]  Wei Cao,et al.  Deep Modeling of Group Preferences for Group-Based Recommendation , 2014, AAAI.

[19]  Hendrik T. Macedo,et al.  Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games , 2013, WWW.

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

[21]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[22]  M. E. Shaw.  Group dynamics : the psychology of small group behavior , 1971 .

[23]  Shlomo Berkovsky,et al.  Group-based recipe recommendations: analysis of data aggregation strategies , 2010, RecSys '10.

[24]  Gao Cong,et al.  Attention-based Group Recommendation , 2018, ArXiv.

[25]  Barry Smyth,et al.  CATS: A Synchronous Approach to Collaborative Group Recommendation , 2006, FLAIRS.

[26]  Letizia Tanca,et al.  Recommending New Items to Ephemeral Groups Using Contextual User Influence , 2016, RecSys.

[27]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

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

[29]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[30]  Yongli Ren,et al.  D-CARS: A Declarative Context-Aware Recommender System , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[31]  Kristian J. Hammond,et al.  Flytrap: intelligent group music recommendation , 2002, IUI '02.

[32]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[33]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[34]  Yuan Tian,et al.  Exploring personal impact for group recommendation , 2012, CIKM.

[35]  Gao Cong,et al.  COM: a generative model for group recommendation , 2014, KDD.

[36]  Xue Li,et al.  Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition , 2019, IEEE Transactions on Cybernetics.

[37]  Ling Shao,et al.  Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval , 2018, IEEE Transactions on Image Processing.

[38]  Lin Wu,et al.  Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus , 2015, IEEE Transactions on Image Processing.

[39]  Kai Zheng,et al.  Social Influence-Based Group Representation Learning for Group Recommendation , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[40]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[41]  Francesco Ricci,et al.  Group recommendations with rank aggregation and collaborative filtering , 2010, RecSys '10.

[42]  Tat-Seng Chua,et al.  Item Silk Road: Recommending Items from Information Domains to Social Users , 2017, SIGIR.

[43]  Sebastiano Pizzutilo,et al.  Group modeling in a public space: methods, techniques, experiences , 2005 .

[44]  Mohan S. Kankanhalli,et al.  A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction , 2018, IJCAI.

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

[46]  Joseph F. McCarthy.  MusIcFX: An Arbiter of Group Preferences , 1998 .

[47]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..