Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
This addresses the need for transparent recommendations to improve user satisfaction, though it builds incrementally on existing multi-task and factorization methods.
The paper tackles the problem of generating explainable recommendations by developing a multi-task learning approach that simultaneously predicts user preferences and provides opinionated textual explanations. Experiments on Amazon and Yelp review datasets showed effectiveness in both recommendation and explanation tasks, with user studies confirming practical value.
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.