GenRec: Generative Sequential Recommendation with Large Language Models

TL;DR

GenRec introduces a novel generative approach to sequential recommendation using large language models, framing the task as sequence-to-sequence generation, which achieves state-of-the-art results efficiently in low-resource settings.

Contribution

The paper proposes GenRec, a lightweight generative model that leverages Transformer-based sequence modeling for recommendation, eliminating the need for manual prompts and improving performance.

Findings

GenRec achieves state-of-the-art results on multiple datasets.

The masked item prediction objective significantly boosts performance.

GenRec trains effectively in low-resource environments.

Abstract

Sequential recommendation is a task to capture hidden user preferences from historical user item interaction data and recommend next items for the user. Significant progress has been made in this domain by leveraging classification based learning methods. Inspired by the recent paradigm of 'pretrain, prompt and predict' in NLP, we consider sequential recommendation as a sequence to sequence generation task and propose a novel model named Generative Recommendation (GenRec). Unlike classification based models that learn explicit user and item representations, GenRec utilizes the sequence modeling capability of Transformer and adopts the masked item prediction objective to effectively learn the hidden bidirectional sequential patterns. Different from existing generative sequential recommendation models, GenRec does not rely on manually designed hard prompts. The input to GenRec is textual user item sequence and the output is top ranked next items. Moreover, GenRec is lightweight and requires only a few hours to train effectively in low-resource settings, making it highly applicable to real-world scenarios and helping to democratize large language models in the sequential recommendation domain. Our extensive experiments have demonstrated that GenRec generalizes on various public real-world datasets and achieves state-of-the-art results. Our experiments also validate the effectiveness of the the proposed masked item prediction objective that improves the model performance by a large margin.