Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network

Abstract

Recommender systems (RS) were created to recommend interesting items to users. There are two recommendation techniques: content-based filtering (CBF) and collaborative filtering (CF). CBF makes recommendations based on a user’s past behavior, whereas CF makes a recommendation using past neighbors’ opinions who have similar behavior to the target user. Nowadays, there are many data on social networks, including Tweets on Twitter. Thus, many researchers have studied RS based on Tweets using latent Dirichlet allocation (LDA) to extract latent data from observed data. Nevertheless, those researchers use either CBF or CF with LDA only. However, CBF provides recommendations that are too specific, whereas CF has sparsity and a cold-start problem. Therefore, this research proposes a new method of recommending Tweets based on hybrid RS with LDA (unsupervised topic modeling) and generalized matrix factorization (supervised learning-based neural network). From experimental results, the proposed method outperforms on mean absolute error and coverage.