Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (1806.03568v1)

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.

Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

The paper "Explainable Recommendation via Multi-Task Learning in Opinionated Text Data" addresses the increasingly important issue of explainable recommendation systems within the domain of Information Retrieval and Computing Methodologies. The authors propose a novel solution that enhances both the recommendation process and its interpretability by leveraging multi-task learning and tensor factorization techniques.

Overview

This paper introduces a framework for generating explainable recommendations by integrating two parallel learning tasks: user preference modeling and opinionated content modeling. These tasks are jointly addressed through tensor factorization, with user reviews serving as the primary dataset from which opinions are extracted and analyzed. The fundamental aim of this research is to not only predict user preferences for certain items but also elucidate these recommendations by providing textual explanations at feature levels.

Methodology

The recommendation algorithm is built upon a three-way tensor over users, items, and features, which is expanded to incorporate user opinions as a fourth dimension. This joint factorization employs Tucker decomposition, enabling a shared latent space to represent user-item-feature-opinion relationships. The modularity of the Tucker model allows flexibility in capturing intrinsic user and item variances, while shared latent factors ensure that the link between opinionated text and user preferences enhances the overall recommendation quality.

A non-negative constraint is utilized within the factorization model to ensure the interpretable nature of the feature spaces. Moreover, the paper incorporates Bayesian Personalized Ranking (BPR) to align with ranking losses, offering a principled way to correct any misalignments in predicted item rankings by factoring in unseen user interactions.

Empirical Evaluation

The authors validate their proposed methodology through rigorous experimentation using large datasets from Amazon and Yelp. These experiments demonstrate improved efficacy over existing models such as NMF, BPRMF, and EFM in both recommendation accuracy (as measured by NDCG) and robustness in sparse input conditions. Notably, MTER achieves the highest improvement of over 15% in top-10 recommendation scenarios, showcasing significant enhancement in delivering pertinent results at the highest ranks—an essential feature for user engagement.

The multitask solution outperforms others in predicting the correct opinion phrase for user-given item features, illustrating both its fine-grained modeling capabilities and user-centric explainability.

Implications and Future Work

The practical implications of this research are significant, especially for recommender systems aiming to gain user trust through transparency. By directly associating opinionated explanations with user preferences, the potential to increase user satisfaction is substantial. Moreover, this method lays a foundation for future work in integrating more complex relations, such as social influences and dynamic user-item interactions, into the explainability framework.

Theoretical implications extend towards the scalability and flexibility of using tensor-based models in multi-task environments, potentially influencing other sectors where complex multi-relational datasets are employed.

In conclusion, this paper provides a comprehensive approach to enhancing the interpretability of recommendation systems using tensor factorization and multi-task learning. Future explorations might delve into real-time implementation challenges and the dynamic evolution of user-item relationships to further refine recommendation quality and trust.