Making Use of Reviews for Good Explainable Recommendation
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- 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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- Johannes Kepler University, Linz, Austria
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Association for Computing Machinery
New York, NY, United States
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