Abstract
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 introduceSIGformer, 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.
Sirui Chen
Student
Sirui Chen is a Ph.D. student in ZLST, supervised by Profs. Can Wang, and Profs. Jiawei Chen.
Jiawei Chen
Research Fellow
Bohao Wang
Student
I am a third-year Ph.D. student, and my supervisors are Prof. Chun Chen, Prof. Can Wang, and Prof. Jiawei Chen.
Shen Han
Student
Shen Han is currently a Master student in ZLST Lab, where he is supervised by Prof.Jiawei Chen.
