Information core optimization using Evolutionary Algorithm with Elite Population in recommender systemsTools Mu, Caihong, Cheng, Huiwen, Feng, Wei, Liu, Yi and Qu, Rong (2017) Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems. In: 2017 IEEE Congress on Evolutionary Computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain. Full text not available from this repository.
Official URL: http://ieeexplore.ieee.org/document/7969435/
AbstractRecommender 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.
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