Abstract
With the explosively increase of information and products, recommender systems have played a more and more important role in the recent years. Various recommendation algorithms, such as content-based methods and collaborative filtering methods, have been proposed. There are a number of performance metrics for evaluating recommender systems, and considering only the precision or diversity might be inappropriate. However, to the best of our knowledge, no existing work has considered recommendation with many objectives. In this paper, we model a many-objective search-based recommender system and adopt a recently proposed many-objective evolutionary algorithm to optimize it. Experimental results on the Movielens data set demonstrate that our algorithm performs better in terms of Generational Distance (GD), Inverted Generational Distance (IGD) and Hypervolume (HV) on most test cases.
Original language | English |
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Title of host publication | Proceedings of the 2016 IEEE Congress on Evolutionary Computation |
Publisher | IEEE Computer Society Press |
Pages | 120-126 |
ISBN (Print) | 978-1-5090-0622-9 |
DOIs | |
Publication status | Published - 24 Jul 2016 |
Event | 2016 IEEE Congress on Evolutionary Computation (CEC) - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016 http://www.wcci2016.org/ |
Conference
Conference | 2016 IEEE Congress on Evolutionary Computation (CEC) |
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Country/Territory | Canada |
City | Vancouver |
Period | 24/07/16 → 29/07/16 |
Internet address |