Neil J. Hurley, School of Computer Science and Informatics, Unversity College, Dublin

Towards Diverse Recommendation

In recent years great strides have been made in improving the accuracy of recommender systems from the point-of-view of their ability to predict users' ratings for unrated content given a database of past ratings. In a context where the system should ultimately recommend a list of items to the end-user, such accurate rating predictions can be seen as just one possible input into the decision system that selects the recommended content. It has been recognized for several years now that other qualities of the recommended list are also important in this selection process; it is not simply a matter of recommending those items with highest predicted ratings. In particular, a good system should offer a diverse choice of relevant items, allowing users to select from across their broad range of tastes. It is worth emphasizing that diversifying the recommendation is not simply a matter of selecting a set of highly dissimilar items for recommendation, since relevance is still a primary concern – increasing diversity while maintaining system performance, as measured by a relevance metric is a significant challenge. Research in diverse recommendation is still in an early stage; while a number of algorithms and systems for diverse recommendation have been proposed, many different performance measures and evaluation methodologies are being used making it difficult to compare across different approaches. In this talk, I will attempt to summarize the state-of-the-art in diverse recommendation, bringing together the different approaches that have been proposed in recent years and the various performance measures that have been used. The goal is to set the context and to propose some ideas to generate what should be some interesting and controversial discussions during the remainder of the workshop.

Dr. Neil Hurley graduated with a M.Sc. degree in Mathematical science from University College Dublin (UCD) in 1988. In 1989, he joined Hitachi Dublin Laboratory (HDL), a computer science research laboratory based at Trinity College Dublin. During his time at HDL he worked on various topics including knowledge-based engineering, for which he was awarded a PhD in 1995, and parallel computing. He joined the academic staff in the UCD School of Computer Science and Informatics in 1999, where he has carried out research in topics including information hiding in digital content, distributed systems and graph and network analysis. He started research on recommender systems in 2002 when he first started to look at robustness issues in kNN collaborative filtering algorithms. Since then, he has worked on various topics in recommendation including recommendation diversity. To date, three students have completed their PhDs on topics in recommendation under his supervision. Currently, his major research focus is social network analysis in the Clique Network and Graph Analysis cluster (

DiveRS 2011 - 1st International Workshop on Novelty and Diversity in Recommender Systems
5th ACM Conference on Recommender Systems (RecSys 2011)
Chicago, IL, USA, 23 October 2011