Abstract
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.
Highlights
In an era of big data along with the popularity of the internet, it is becoming more and more difficult and time consuming for people to capture the information and commodities that they are really interested in
All above methods are based on a key idea that the historical information from some “expert users” or “core users” is more reliable, objective and positive, is key and of higher importance to impact upon the performance of recommender systems
Considering that the information core usually does not change very often in real world, and offline optimization can be used, we propose a novel evolutionary algorithm with Elite Population (EA-EP) in this paper to investigate its global optimization in the problem of information core optimization
Summary
In an era of big data along with the popularity of the internet, it is becoming more and more difficult and time consuming for people to capture the information and commodities that they are really interested in. We propose a general method to identify core users by modelling this problem as a combinatorial optimization problem and solve it by a novel evolutionary algorithm (EA). In this paper, aiming at solving the single-objective problem of finding information core, we propose a novel mechanism of elite population, i.e., in each generation, all the elite individuals are sorted in a descending order according to their fitness values, and every two neighboring elite individuals in the queue are combined with each other using crossover operator, while the common individuals could not take part in the crossover procedure unless they are improved to become an elite individual by the mutation operator in the following generations This mechanism ensures that high-quality individuals have more chances to take part in the evolution, to guide the search to the promising regions more quickly.
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