FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement

TL;DR

This paper improves baseline models for recommender systems by tuning and combining language models with collaborative filtering, introducing Flare, a hybrid architecture that handles large datasets and supports user feedback for refined recommendations.

Contribution

The paper revises baseline models for item ID-only and textual descriptions, and introduces Flare, a novel hybrid recommender integrating language models with collaborative filtering using a Perceiver network.

Findings

Revised Bert4Rec outperforms previous benchmarks.

Flare achieves competitive results on large-scale datasets.

Flare supports critiquing for user feedback and recommendation refinement.

Abstract

Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an item ID-only model, such as Bert4Rec. In this work, we revisit the often-used Bert4Rec baseline and show that with further tuning, Bert4Rec significantly outperforms previously reported numbers, and in some datasets, is competitive with state-of-the-art models. With revised baselines for item ID-only models, this paper also establishes new competitive results for architectures that combine IDs and textual descriptions. We demonstrate this with Flare (Fusing Language models and collaborative Architectures for Recommender Enhancement). Flare is a novel hybrid sequence recommender that integrates a language model with a collaborative filtering model using a Perceiver network. Prior studies focus evaluation on datasets with limited-corpus size, but many commercially-applicable recommender systems common on the web must handle larger corpora. We evaluate Flare on a more realistic dataset with a significantly larger item vocabulary, introducing new baselines for this setting. This paper also showcases Flare's inherent ability to support critiquing, enabling users to provide feedback and refine recommendations. We leverage critiquing as an evaluation method to assess the model's language understanding and its transferability to the recommendation task.