FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement

• URL: http://arxiv.org/abs/2409.11699v1
• Date: Wed, 18 Sep 2024 04:43:41 GMT
• Title: FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement
• Authors: Liam Hebert, Marialena Kyriakidi, Hubert Pham, Krishna Sayana, James Pine, Sukhdeep Sodhi, Ambarish Jash,
• Abstract summary: Flare is a novel hybrid recommender that integrates a language model (mT5) with a collaborative filtering model (Bert4Rec) using a Perceiver network. We conduct a two-stage evaluation, first assessing Flare's performance against established baselines on smaller datasets. We then evaluate Flare on a larger, more realistic dataset with a significantly larger item vocabulary.
• Score: 0.7372033475418547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hybrid recommender systems, combining item IDs and textual descriptions, offer potential for improved accuracy. However, previous work has largely focused on smaller datasets and model architectures. This paper introduces Flare (Fusing Language models and collaborative Architectures for Recommender Enhancement), a novel hybrid recommender that integrates a language model (mT5) with a collaborative filtering model (Bert4Rec) using a Perceiver network. This architecture allows Flare to effectively combine collaborative and content information for enhanced recommendations. We conduct a two-stage evaluation, first assessing Flare's performance against established baselines on smaller datasets, where it demonstrates competitive accuracy. Subsequently, we evaluate Flare on a larger, more realistic dataset with a significantly larger item vocabulary, introducing new baselines for this setting. Finally, we showcase Flare's inherent ability to support critiquing, enabling users to provide feedback and refine recommendations. We further leverage critiquing as an evaluation method to assess the model's language understanding and its transferability to the recommendation task.

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