Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

2018-06-10 · Nan Wang, Hongning Wang, Yiling Jia, Yue Yin

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.

📄 PDF Abstract BibTeX arXiv:1806.03568

Code (1)

mythwn/mter

Tasks

Explainable RecommendationMulti-Task Learning

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2025-10-03 · Bo Ma, LuYao Liu, ZeHua Hu, Simon Lau arxiv

Recent advances in Large Language Models (LLMs) have opened new possibilities for recommendation systems, though current approaches such as TALLRec face challenges in explainability and cold-start scenarios. We present E…

Recommendation Systems Movie Recommendation Transfer Learning

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2020-06-03 · Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao 외

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in particular, may suffer from both explanation …

Collaborative Filtering Decision Making Explainable Recommendation Fairness+3