Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

TL;DR

This paper presents a multi-task learning approach that jointly models user preferences and opinionated content to generate explainable recommendations, improving user satisfaction and decision-making.

Contribution

It introduces a novel joint tensor factorization method that integrates recommendation and explanation tasks within a multi-task learning framework.

Findings

Effective in recommending items with feature-level explanations

Outperforms existing recommendation algorithms in accuracy

User study confirms practical value of explanations

Abstract

Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation} and \textit{opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.