An Improved Collaborative Filtering Algorithm Based on Sparse Dataset's Optimization with User's Browser Information

Abstract

Collaborative filtering technology is the mainstream recommendation technology in personalized recommendation system, the sparsity of the dataset plays a leading role in the prediction accuracy of the collaborative filtering algorithm. Virtual data filling and neighbors' calculation etc. are adopted to solve the sparsity problem in traditional methods, which lacked of dynamic changes of rating data and objectivity. For the deficiencies of the traditional methods, making use of the data redundancy and dynamic changes in Big Data environment, to improve the sparse dataset, this paper proposes an improved collaborative filtering algorithm based on optimizing sparse dataset through user's browser information. This approach gets data related with user objective score from various fields through user's IP address to fill the dataset and reduce the sparsity of the dataset of candidate neighbors. The algorithm is compared with other classic algorithms on the performance and analyzing the result in the case of sparse dataset. The experiments results show that the algorithm can effectively reduce the sparsity of the data set, and improve the quality of recommendation system.