A Recommendation Algorithm Based on Item Genres Preference and GBRT

Abstract

The most mature and widely used collaborative filtering algorithm is facing the problem of data sparsity, which is not conducive to the acquisition of user preferences, thus affecting the recommendation effect. Introducing item genres into recommendation algorithm can reduce the impact of data sparsity on recommendation effect. The personalized preferences of users can be extracted more effectively from the preference information of item genres, and the recommendation accuracy can be further improved. On this basis, this paper proposes a recommendation algorithm based on item genres preference and GBRT, which divides similar users by K-means clustering algorithm, extracts user preferences of item genres and auxiliary features, and establishes a rating prediction model combined with GBRT algorithm. The experiments on the common datasets of Movie Lens 100K and Movie Lens 1M show that the proposed algorithm achieves 0.8%-7% optimization on the evaluation index MAE, which indicates that the impact of data sparsity is reduced to a certain extent and the recommendation efficiency is better than the existing recommendation algorithm.