Collaborative Filtering Algorithm Based on Improved Time Function and User Similarity

Abstract

As a typical representative of information filtering technology in the era of big data, a recommendation system is an important means to solve the problem of information overload. A collaborative filtering recommendation algorithm is one of the important technologies to realize the recommendation system, but the traditional collaborative filtering algorithm only considers the similarity of ratings between users. As the number of users and the number of items increases, it faces user interest drift and reduced recommendation Precision, and other issues. In this regard, a collaborative filtering algorithm based on improved time function and user similarity is proposed. First, considering that user interests will dynamically change over time, this paper introduces an improved time function in the traditional scoring similarity; secondly, considering the impact of the number of items evaluated by users on the calculation of similarity measurement, this paper takes the Pearson correlation coefficient weighted scoring into account; Finally, the fusion recommendation is based on the improved time function and the weighted Pearson correlation coefficient to improve the Precision of recommendation prediction. This paper conducts simulation experiments on MovieLens 100K and 1M data sets respectively. The results show that, compared with the traditional collaborative filtering recommendation algorithm, combining the improved time function and the weighted Pearson correlation coefficient can effectively improve the recommendation Precision.