UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Temporal diversity in recommender systems

Lathia, N; Hailes, S; Capra, L; Amatriain, X; (2010) Temporal diversity in recommender systems. Presented at: UNSPECIFIED.

Full text not available from this repository.


Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the system's top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy. © 2010 ACM.

Type: Conference item (UNSPECIFIED)
Title: Temporal diversity in recommender systems
ISBN-13: 9781605588964
DOI: 10.1145/1835449.1835486
Keywords: Evaluation, Recommender systems
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/75910
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item