FineRec:Exploring Fine-grained Sequential Recommendation

TL;DR

FineRec leverages attribute-opinion pairs from reviews using large language models to create attribute-specific graphs, enabling more precise sequential recommendations by capturing fine-grained user preferences and item characteristics.

Contribution

The paper introduces a novel framework that extracts attribute-opinion pairs and constructs attribute-specific graphs for improved sequential recommendation accuracy.

Findings

Outperforms existing state-of-the-art methods on real-world datasets.

Effectively captures fine-grained user preferences and item features.

Demonstrates the benefits of attribute-specific modeling in recommendation tasks.

Abstract

Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. To tackle the diversity of opinions, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several realworld datasets demonstrate the superiority of our FineRec over existing state-of-the-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.