Search Based Recommender System Using Many-objective Evolutionary Algorithm

Bingdong Li, Chao Qian, Jinlong Li, Ke Tang, Xin Yao

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

4 Citations (Scopus)
260 Downloads (Pure)


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 languageEnglish
Title of host publicationProceedings of the 2016 IEEE Congress on Evolutionary Computation
PublisherIEEE Computer Society Press
ISBN (Print)978-1-5090-0622-9
Publication statusPublished - 24 Jul 2016
Event2016 IEEE Congress on Evolutionary Computation (CEC) - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016


Conference2016 IEEE Congress on Evolutionary Computation (CEC)
Internet address


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