Capsmf: a novel product recommender system using deep learning based text analysis model

Abstract

Researchers and data scientists have developed different Recommender System Algorithms such as Content-Based and Collaborative-Based in order to filter a large amount of information available on the internet and hence, recommend only the relevant and essential content based on the personalized interests of users. Information acquired explicitly by collecting users’ ratings for an item lead to the problem of data sparsity. Many researchers have been currently working towards the improvement of rating prediction accuracy by integrating the auxiliary information along with the ratings provided by the users. This paper proposes a novel product recommender system called as “CapsMF”, it applies the advanced neural network architecture Capsule Networks (Caps) for document representation, and MF represents Matrix factorization. In the proposed approach, we have enhanced a deep neural network text analysis model by adding a newly discovered neural network architecture; Capsule Networks stacked on bi-directional Recurrent Neural Network (Bi-RNN) for the robust representation of textual descriptions of items and users. The Deep Neural Network text analysis model is integrated with the Probabilistic Matrix Factorization to generate improved recommendations. The experiment has been performed on two real amazon datasets resulting in the enhancement of rating prediction accuracy, the recall, and the precision of top-n recommendations, in comparison to the basic and hybrid Recommendation System Algorithms. Also, text analysis model involving Capsule Networks stacked with Recurrent Neural Networks (RNNs) have outperformed the baseline models that have single Convolutional Neural Networks (CNN) or CNN combined with Bi-RNN in text analysis.