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Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems

Mu, Caihong; Cheng, Huiwen; Feng, Wei; Liu, Yi; Qu, Rong


Caihong Mu

Huiwen Cheng

Wei Feng

Yi Liu

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Associate Professor


Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for making recommendations. Finding the information core is of great interests to greatly increase the speed of online recommendation. There is no general method to identify core users in the existing literatures. In this paper, a general method of finding information core is proposed by modelling this problem as a combinatorial optimization problem. A novel Evolutionary Algorithm with Elite Population (EA-EP) is presented to search for the information core, where an elite population with a new crossover mechanism named as ordered crossover is used to accelerate the evolution. Experiments are conducted on Movielens (100k) to validate the effectiveness of our proposed algorithm. Results show that EA-EP is able to effectively identify core users and leads to better recommendation accuracy compared to several existing greedy methods and the conventional collaborative filter (CF). In addition, EA-EP is shown to significantly reduce the time of online recommendation.


Mu, C., Cheng, H., Feng, W., Liu, Y., & Qu, R. (2017). Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems.

Conference Name 2017 IEEE Congress on Evolutionary Computation (CEC 2017)
End Date Jun 8, 2017
Acceptance Date Mar 5, 2017
Publication Date Jul 7, 2017
Deposit Date Sep 19, 2017
Publicly Available Date Sep 19, 2017
Peer Reviewed Peer Reviewed
Keywords evolutionary algorithm, elite population, recommender system, core users
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Additional Information doi:10.1109/CEC.2017.7969435. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works


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