Recommendations by Concise User Profiles from Review Text

• URL: http://arxiv.org/abs/2311.01314v3
• Date: Thu, 27 Feb 2025 16:36:08 GMT
• Title: Recommendations by Concise User Profiles from Review Text
• Authors: Ghazaleh Haratinezhad Torbati, Anna Tigunova, Andrew Yates, Gerhard Weikum,
• Abstract summary: This work addresses the difficult and underexplored case of users who have very sparse interactions but post informative review texts.<n> feeding the full text of all reviews through an LLM has a weak signal-to-noise ratio and incurs high costs of processed tokens.<n>It presents a light-weight framework, called CUP, which first computes concise user profiles and feeds only these into the training of transformer-based recommenders.
• Score: 24.408292545170944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems perform well for popular items and users with ample interactions (likes, ratings etc.). This work addresses the difficult and underexplored case of users who have very sparse interactions but post informative review texts. This setting naturally calls for encoding user-specific text with large language models (LLM). However, feeding the full text of all reviews through an LLM has a weak signal-to-noise ratio and incurs high costs of processed tokens. This paper addresses these two issues. It presents a light-weight framework, called CUP, which first computes concise user profiles and feeds only these into the training of transformer-based recommenders. For user profiles, we devise various techniques to select the most informative cues from noisy reviews. Experiments, with book reviews data, show that fine-tuning a small language model with judiciously constructed profiles achieves the best performance, even in comparison to LLM-generated rankings.

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