DUM: Diversity-Weighted Utility Maximization for Recommendations

The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the utility of the recommended items subject to the diversity of user's tastes, and show that an optimal solution to this problem can be found greedily. We evaluate the proposed method in two online user studies as well as in an offline analysis incorporating a number of evaluation metrics. The results of evaluations show the superiority of our method over a number of baselines.

[1]  Pablo Castells,et al.  Novelty and diversity metrics for recommender systems: Choice, discovery and relevance , 2011 .

[2]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[3]  Jun Yu,et al.  Latent dirichlet allocation based diversified retrieval for e-commerce search , 2014, WSDM.

[4]  Yiming Yang,et al.  Learning to rank relevant and novel documents through user feedback , 2010, CIKM.

[5]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[6]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[7]  Mi Zhang,et al.  Avoiding monotony: improving the diversity of recommendation lists , 2008, RecSys '08.

[8]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[9]  Sreenivas Gollapudi,et al.  Diversifying search results , 2009, WSDM '09.

[10]  Martin Halvey,et al.  Diversity, Assortment, Dissimilarity, Variety: A Study of Diversity Measures Using Low Level Features for Video Retrieval , 2009, ECIR.

[11]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[12]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[13]  Sreenivas Gollapudi,et al.  An axiomatic approach for result diversification , 2009, WWW '09.

[14]  Fabrizio Silvestri,et al.  Efficient Diversification of Web Search Results , 2011, Proc. VLDB Endow..

[15]  Saul Vargas,et al.  Explicit relevance models in intent-oriented information retrieval diversification , 2012, SIGIR '12.

[16]  Yisong Yue,et al.  Linear Submodular Bandits and their Application to Diversified Retrieval , 2011, NIPS.

[17]  Daniele Quercia,et al.  Auralist: introducing serendipity into music recommendation , 2012, WSDM '12.

[18]  Saul Vargas,et al.  Rank and relevance in novelty and diversity metrics for recommender systems , 2011, RecSys '11.

[19]  Christian Bauckhage,et al.  Convex non-negative matrix factorization for massive datasets , 2011, Knowledge and Information Systems.

[20]  Jack Edmonds,et al.  Submodular Functions, Matroids, and Certain Polyhedra , 2001, Combinatorial Optimization.

[21]  Craig MacDonald,et al.  Exploiting query reformulations for web search result diversification , 2010, WWW '10.

[22]  Charles L. A. Clarke,et al.  Novelty and diversity in information retrieval evaluation , 2008, SIGIR '08.

[23]  Dietmar Jannach,et al.  What Recommenders Recommend - An Analysis of Accuracy, Popularity, and Sales Diversity Effects , 2013, UMAP.

[24]  Filip Radlinski,et al.  Learning diverse rankings with multi-armed bandits , 2008, ICML '08.

[25]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[26]  Jade Goldstein-Stewart,et al.  The use of MMR, diversity-based reranking for reordering documents and producing summaries , 1998, SIGIR '98.

[27]  Pablo Castells,et al.  Personalized diversification of search results , 2012, SIGIR '12.

[28]  Sean M. McNee,et al.  Being accurate is not enough: how accuracy metrics have hurt recommender systems , 2006, CHI Extended Abstracts.

[29]  Nick Craswell,et al.  An experimental comparison of click position-bias models , 2008, WSDM '08.

[30]  Zheng Wen,et al.  Diversified Utility Maximization for Recommendations , 2014, RecSys Posters.