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CAESAR: context-aware explanation based on supervised attention for service recommendations

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Abstract

Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.

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Notes

  1. https://www.tripadvisor.com

  2. https://www.yelp.com/dataset/challenge

  3. https://www.python.org

  4. https://www.tensorflow.org

  5. The results on Yelp-2019 dataset are similar, so we do not show here.

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Authors and Affiliations

  1. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

    Lei Li & Li Chen

  2. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Ruihai Dong

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  1. Lei Li
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  2. Li Chen
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  3. Ruihai Dong
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Correspondence to Lei Li.

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Li, L., Chen, L. & Dong, R. CAESAR: context-aware explanation based on supervised attention for service recommendations. J Intell Inf Syst 57, 147–170 (2021). https://doi.org/10.1007/s10844-020-00631-8

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