Boosting the Rating Prediction with Click Data and Textual Contents

TL;DR

This paper introduces TCMF, a novel matrix factorization model that integrates user ratings, textual contents, and item co-click data to improve rating prediction and item representation learning.

Contribution

The paper proposes TCMF, a new model that leverages item co-click information alongside textual contents and ratings, enhancing prediction accuracy and item representation quality.

Findings

TCMF outperforms existing methods in rating prediction on real-world datasets.

Incorporating co-click data captures item relationships beyond textual content.

Learned item representations are effective for classification tasks.

Abstract

Matrix factorization (MF) is one of the most efficient methods for rating predictions. MF learns user and item representations by factorizing the user-item rating matrix. Further, textual contents are integrated to conventional MF to address the cold-start problem. However, the textual contents do not reflect all aspects of the items. In this paper, we propose a model that leverages the information hidden in the item co-click (i.e., items that are often clicked together by a user) into learning item representations. We develop TCMF (Textual Co Matrix Factorization) that learns the user and item representations jointly from the user-item matrix, textual contents and item co-click matrix built from click data. Item co-click information captures the relationships between items which are not captured via textual contents. The experiments on two real-world datasets MovieTweetings, and Bookcrossing) demonstrate that our method outperforms competing methods in terms of rating prediction. Further, we show that the proposed model can learn effective item representations by comparing with state-of-the-art methods in classification task which uses the item representations as input vectors.