Ga Wu
Harold Soh
ABSTRACT
Critiquing is a method for conversational recommendation that adapts recommendations in response to user preference feedback regarding item attributes. Historical critiquing methods were largely based on constraint- and utility-based methods for modifying recommendations w.r.t. these critiqued attributes. In this paper, we revisit the critiquing approach from the lens of deep learning based recommendation methods and language-based interaction. Concretely, we propose an end-to-end deep learning framework with two variants that extend the Neural Collaborative Filtering architecture with explanation and critiquing components. These architectures not only predict personalized keyphrases for a user and item but also embed language-based feedback in the latent space that in turn modulates subsequent critiqued recommendations. We evaluate the proposed framework on two recommendation datasets containing user reviews. Empirical results show that our modified NCF approach not only provides a strong baseline recommender and high-quality personalized item keyphrase suggestions, but that it also properly suppresses items predicted to have a critiqued keyphrase. In summary, this paper provides a first step to unify deep recommendation and language-based feedback in what we hope to be a rich space for future research in deep critiquing for conversational recommendation.
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Index Terms
- Deep language-based critiquing for recommender systems
Recommendations
Deep Critiquing for VAE-based Recommender Systems
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information RetrievalProviding explanations for recommended items not only allows users to understand the reason for receiving recommendations but also provides users with an opportunity to refine recommendations by critiquing undesired parts of the explanation. While much ...
A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems
RecSys '20: Proceedings of the 14th ACM Conference on Recommender SystemsCritiquing is a method for conversational recommendation that incrementally adapts recommendations in response to user preference feedback. Specifically, a user is iteratively provided with item recommendations and attribute descriptions for those ...
Latent Linear Critiquing for Conversational Recommender Systems
WWW '20: Proceedings of The Web Conference 2020Critiquing is a method for conversational recommendation that iteratively adapts recommendations in response to user preference feedback. In this setting, a user is iteratively provided with an item recommendation and attribute description for that item;...
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