Diversity and Serendipity in Recommender Systems

The present age of digital information has presented a heterogeneous online environment which makes it a formidable mission for a noble user to search and locate the required online resources timely. Recommender systems were implemented to rescue this information overload issue. However, majority of recommendation algorithms focused on the accuracy of the recommendations, leaving out other important aspects in the definition of good recommendation such as diversity and serendipity. This results in low coverage, long-tail items often are left out in the recommendations as well. In this paper, we present and explore a recommendation technique that ensures that diversity, accuracy and serendipity are all factored in the recommendations. The proposed algorithm performed comparatively well as compared to other algorithms in literature.

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

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

[3]  Ralph Bergmann,et al.  Diversity-Conscious Retrieval from Generalized Cases: A Branch and Bound Algorithm , 2003, ICCBR.

[4]  David McSherry,et al.  Diversity-Conscious Retrieval , 2002, ECCBR.

[5]  Gediminas Adomavicius,et al.  Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques , 2012, IEEE Transactions on Knowledge and Data Engineering.

[6]  Erik Brynjolfsson,et al.  Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales , 2011, Manag. Sci..

[7]  Gediminas Adomavicius,et al.  Maximizing Aggregate Recommendation Diversity: A Graph-Theoretic Approach , 2011, RecSys 2011.

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

[9]  Barry Smyth,et al.  Improving Recommendation Diversity , 2001 .

[10]  Shuaiqiang Wang,et al.  A survey of serendipity in recommender systems , 2016, Knowl. Based Syst..

[11]  Katja Niemann,et al.  A new collaborative filtering approach for increasing the aggregate diversity of recommender systems , 2013, KDD.

[12]  Shuaiqiang Wang,et al.  Challenges of Serendipity in Recommender Systems , 2016, WEBIST.

[13]  Zhong Chen,et al.  Draining Algorithm for the Maximum Flow Problem , 2009, 2009 WRI International Conference on Communications and Mobile Computing.

[14]  Diarmuid O'Donoghue.  Proceedings of 12th Irish Conference on Artificial Intelligence & Cognitive Science (AICS - 2001) , 2001 .

[15]  Barry Smyth,et al.  Similarity vs. Diversity , 2001, ICCBR.