SIGformer: Sign-aware Graph Transformer for Recommendation

In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences. However, the existing efforts...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-05
Main Authors: Chen, Sirui, Chen, Jiawei, Zhou, Sheng, Wang, Bohao, Shen, Han, Su, Chanfei, Yuan, Yuqing, Wang, Can
Format: Article
Language:English
Subjects:
Information retrieval
Negative feedback
Recommender systems
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences. However, the existing efforts to incorporate both types of feedback are sparse and face two main limitations: 1) They process positive and negative feedback separately, which fails to holistically leverage the collaborative information within the signed graph; 2) They rely on MLPs or GNNs for information extraction from negative feedback, which may not be effective. To overcome these limitations, we introduce SIGformer, a new method that employs the transformer architecture to sign-aware graph-based recommendation. SIGformer incorporates two innovative positional encodings that capture the spectral properties and path patterns of the signed graph, enabling the full exploitation of the entire graph. Our extensive experiments across five real-world datasets demonstrate the superiority of SIGformer over state-of-the-art methods. The code is available at https://github.com/StupidThree/SIGformer.
ISSN:2331-8422
DOI:10.48550/arxiv.2404.11982