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. © 2016 IEEE.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE Congress on Evolutionary Computation, CEC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 120-126 |
| Number of pages | 7 |
| ISBN (Print) | 9781509006229 |
| DOIs | |
| Publication status | Published - Jul 2016 |
| Externally published | Yes |
Bibliographical note
The authors would like to thank Prof. Maoguo Gong from Xidian University, Xi'an, China for kindly sharing the sourcecode with us.Funding
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61329302 and Grant No. 61573328), the Program for New Century Excellent Talents in University (Grant No. NCET-12-0512) and EPSRC (Grant No. EP/K001523/1) and the Natural Science Key Research Project for Higher Education Institutions of Anhui Province (KJ2016A438) and the Fundamental Research Funds for the Central Universities (WK2150110002). Xin Yao was also supported by a Royal Society Wolfson Research Merit Award.