Shunsuke Kido
ABSTRACT
Reviews are used in generating explainable recommendation. However, the use of reviews has so far not been adequately addressed. In this paper, we examine methods that make use of reviews effectively. There is a trade-off between the number and quality of reviews to use, that is, we should like to use reviews as many as possible to generate explainable recommendation, however in a large number of reviews there can be low quality ones, which can cause low quality explainable recommendation generation. We discuss new methods that use not only reviews written by a user but also those utilized by the user to generate good explainable recommendation. Our methods can be applied to different explainable recommender approaches, which is shown by adopting two state-of-the-art explainable recommender approaches in this paper. Experimental results demonstrate that our methods can be of benefit to existing explainable recommender approaches as regards both recommendation and its explanation qualities.
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Index Terms
- Making Use of Reviews for Good Explainable Recommendation
Recommendations
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrievalCollaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, ...
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalExplaining 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 ...
SAER: Sentiment-Opinion Alignment Explainable Recommendation
Database Systems for Advanced ApplicationsAbstractExplainable recommendation systems not only provide users with recommended results but also explain why they are recommended. Most existing explainable recommendation methods leverage sentiment analysis to help users understand reasons for ...
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