Mutual information-based recommender system using autoencoder

Abstract

Nowadays, most of the websites like Amazon, YouTube and Netflix use collaborative filtering methods to recommend various types of items to users. There are two principal categories of collaborative filtering; memory-based and model-based. The memory-based methods use the users’ similarity measures and have several advantages over the model-based techniques, including being easily explained and easy modeling updates with new ratings and items. However, the memory-based methods’ performance reduces when the data is sparse, and unlike the model-based methods, memory-based methods are not scalable. In this paper, we propose a method that exploit the benefits of both similarity-based and model-based approaches. We address both the reliability and the online updating problems based on a novel user-similarity based method. To calculate the new similarity metric we use the predicted user rating vectors in the autoencoder’s output and apply mutual information to the predicted vectors in order to find similar users. We depict a similarity graph according to the mutual information rate, which is calculated for each pair of users. We implement the proposed method on the Netflix movie recommendation dataset. According to our experiments, the proposed approach has a significant advantage over the other methods, such as the standard autoencoder, the matrix factorization, and the similarity-based methods.