A Collaborative Filtering Algorithm of Weighted Information Entropy and User Attributes
Xu Wang · 2017
Collaborative filtering algorithm is one of the most successful technologies in recommendation system, and similarity calculation is the core. To solve the problem that the traditional similarity calculation method is not accurate in the case of sparse data, a cooperative filtering recommendation algorithm combining weighted entropy and user attribute is proposed in this paper. This algorithm applies the information entropy theory in the information theory to the similarity calculation, and takes into account the influence of the user interest and user difference on the similarity calculation. The simulation results show that the algorithm has a smaller MAE value than the recommended algorithm based on Pearson correlation coefficient and the cosine similarity, thus it improves the recommended quality.