An efficient collaborative filtering algorithm using SVD-free latent Semantic indexing and particle swarm optimization

The amount of accessible information in the Internet increases every day and it becomes greatly difficult to deal with such a huge source of information. Consequently, Recommender Systems (RS) which are considered as powerful tools for Information Retrieval (IR), can access these available information efficiently. Unfortunately, the recommendations accuracy is seriously affected by the problems of data sparsity and scalability. Additionally, the time of recommendations is very essential in the Recommender Systems. Therefore, we propose a proficient dimensionality reduction-based Collaborative Filtering (CF) Recommender System. In this technique, the Singular Value Decomposition-free (SVD-free) Latent Semantic Indexing (LSI) is utilized to obtain a reduced data representation solving the sparsity and scalability limitations. Also, the SVD-free extremely reduce the time and memory usage required for dimensionality reduction employing the partial symmetric Eigenproblem. Moreover, to estimate the optimal number of reduced dimensions which greatly influences the system accuracy, the Particle Swarm Optimization (PSO) algorithm is utilized to automatically obtain it. As a result, the proposed technique enormously increases the recommendations prediction quality and speed. In additions, it decreases the memory requirements. To show the efficiency of the proposed technique, we employed it to the MovieLens dataset and the results was very promising.