Book recommendation system based on an optimized collaborative filtering algorithm

Abstract

Collaborative filtering is widely applied in recommendation systems. The traditional method usually adopts the cosine similarity algorithm or Pearson algorithm, but a sparse rating matrix may lead to inaccurate recommendation results. The optimized algorithm adds penalty terms according to the number of score vector elements to reduce the impact of sparsity. More purchase behaviors are taken into account in the optimization algorithm, including user activity, product popularity, and the time cost of user preferences. Due to the validity of the data set, the top-k method is adopted to select k users with the highest similarity (1) as the recommendation basis. Compared with the traditional method, the numerical results have a lower root mean squared error, and the algorithm execution time is significantly shortened. The optimized collaborative filtering algorithm can effectively alleviate the impact of sparsity and consider more purchasing behaviors, thus improving the algorithm efficiency and rating reliability of the book recommendation system.