Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
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DOI：
10.1145/3209978.3210010
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Nan Wang;Hongning Wang;Yiling Jia;Yue Yin
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文献类型：
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作者：
Nan Wang;Hongning Wang;Yiling Jia;Yue Yin
解释自动生成的建议使用户能够更明智、更准确地决定要使用哪些结果，从而提高他们的满意度。在这项工作中，我们开发了一个针对解释性推荐的多任务学习解决方案。通过联合张量分解将用户偏好建模用于推荐和观点内容建模用于解释这两个伴随的学习任务进行集成。结果，该算法不仅预测用户对项目列表(即，推荐)的偏好，而且还预测用户将如何在特征级别欣赏特定项目，即，固执己见的文本解释。在亚马逊和Yelp两大评论集上的广泛实验证实了我们的解决方案在推荐和解释任务中的有效性，并与现有的几种推荐算法进行了比较。我们广泛的用户研究清楚地证明了由我们的算法生成的可解释的推荐的实用价值。
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 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.