On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance
This addresses the challenge of making explanations in recommender systems more effective and measurable, which is important for user trust and decision-making, though it is incremental in extending existing ranking methods.
The paper tackles the problem of evaluating and improving explanations in recommender systems by formulating explanation as a ranking task, enabling standard evaluation via metrics like NDCG, and proposes a joint-ranking method that improves recommendation performance, with experiments on three large datasets verifying effectiveness.
Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanation as a side output 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, how the explanations impact the recommendation performance is less investigated. In this paper, explaining recommendations is formulated as a ranking task, and learned from data, similar to item ranking for recommendation. This makes it possible for standard evaluation of explanations via ranking metrics (e.g., NDCG). Furthermore, this paper extends traditional item ranking to an item-explanation joint-ranking formalization to study if purposely selecting explanations could reach certain learning goals, e.g., improving recommendation performance. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be inevitably severer than that in traditional user-item interaction data, since not every user-item pair can be associated with all explanations. To mitigate this issue, this paper proposes to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. Experiments on three large datasets verify the solution's effectiveness on both explanation ranking and item recommendation.