Learning to Explain Recommendations

Explaining to users why some items are recommended is critical, as it helps users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanations as side outputs of the recommendation model, which has two problems: (1) it is difficult to evaluate the produced explanations because they are usually model-dependent, and (2) as a result, the possible impacts of those explanations are less investigated. To address the evaluation problem, we propose learning to explain for explainable recommendation. The basic idea is to train a model that selects explanations from a collection as a ranking-oriented task. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be severer than that in traditional user-item relation data, since not every user-item pair can associate with multiple explanations. To mitigate this issue, we propose to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. To further investigate the impacts of explanations, we extend the traditional item ranking of recommendation to an item-explanation joint-ranking formalization. We study if purposely selecting explanations could achieve certain learning goals, e.g., in this paper, improving the recommendation performance. Experiments on three large datasets verify our solution's effectiveness on both item recommendation and explanation ranking. In addition, our user-item-explanation datasets open up new ways of modeling and evaluating recommendation explanations. To facilitate the development of explainable RS, we will make our datasets and code publicly available.