Natural Language User Profiles for Transparent and Scrutable Recommendations
CoRR(2024)
摘要
Current state-of-the-art recommender systems predominantly rely on either
implicit or explicit feedback from users to suggest new items. While effective
in recommending novel options, these conventional systems often use
uninterpretable embeddings. This lack of transparency not only limits user
understanding of why certain items are suggested but also reduces the user's
ability to easily scrutinize and edit their preferences. For example, if a user
has a change in interests, they would need to make significant changes to their
interaction history to adjust the model's recommendations. To address these
limitations, we introduce a novel method that utilizes user reviews to craft
personalized, natural language profiles describing users' preferences. Through
these descriptive profiles, our system provides transparent recommendations in
natural language. Our evaluations show that this novel approach maintains a
performance level on par with established recommender systems, but with the
added benefits of transparency and user control. By enabling users to
scrutinize why certain items are recommended, they can more easily verify,
adjust, and have greater autonomy over their recommendations.
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