Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm

Abstract

With the unprecedented development in the internet technology, the information overload issues have become more and more complex, resulting in users being unable to obtain the target information accurately and effectively in selecting the required information from a large pool of surfed data. In view of this a recommendation system can be used to predict the user's selection probability for different potential objects as an important tool, which can help to solve the information overload issues. So far, many personalized recommendation algorithms based on bipartite graphS have been proposed, most of which are based on the similarity degree among users or items, such as collaborative filtering (CF), mass diffusion (MD) and heat conduction (HC). Among many recommendation algorithms, the performances of algorithms are varied. MD algorithm has high recommendation accuracy but poor diversity, while HC algorithm has good diversity but low accuracy. In order to solve the dilemma in accuracy and diversity, some hybrid recommendation algorithm have been proposed. This paper has mainly focused on the hybrid recommendation algorithm HHM, and pointed out its shortcomings. Based on the reconsideration of the effect of item popularity in the recommendation process, an improved hybrid recommendation algorithm using dual parameter called IHM was proposed. The particle swarm optimization (PSO) algorithm was applied to the parameter optimal of the hybrid recommendation algorithm to obtain the parameters of the algorithm. Experiments on 3 real datasets indicated that the IHM algorithm is better than HHM algorithms in terms of the recommendation accuracy, diversity and novelty. Meanwhile, the IHM algorithm can also improve the recommendation for items with lower popularity and solve the cold start problem.