Improving Collaborative Filter Using BERT

Abstract

With the increasing number of books published and the difficulty of obtaining appropriate research attention, the recommendation systems can increase the affordability and availability of these books. In this work, we expand our work to enhance the accuracy of book collaborative filtering by applying semantic similarity to book summaries, in addition to that addressing major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of three stages: preprocessing, building the system, and evaluation. The technologies used in the pre-processing stage included reduction and normalization. The construction system is divided into two phases: semantic similarity and recommendation. The semantic similarity is done by using BERT for sentence embedding and cosine similarity to calculate the similarity between sentences. During the recommendation phase by using CF based on KNN. In the evaluation stage, classification accuracy metrics had been used. The proposed approach improved the accuracy of the book recommendation system and increased the accuracy to 0.89 compared to previous works on a dataset of 271,000 book summaries. The proposed approach yielded better results due to avoiding problems in previous work, such as scalability and sparsity, by using BERT with CF based KNN. Filtering the data using BERT and the KNN algorithm in the CF added strength to the recommendation, which led to an increase in the accuracy rate.