Summary of Contrastive Sequential Interaction Network Learning on Co-evolving Riemannian Spaces, by Li Sun et al.
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces
by Li Sun, Junda Ye, Jiawei Zhang, Yong Yang, Mingsheng Liu, Feiyang Wang, Philip S.Yu
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel model called Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces (CSINCERE) to address significant issues in predicting future interactions. The proposed method introduces co-evolving representation spaces and a co-contrastive learning approach to sequentially interact with each other without requiring label information. The model is designed to learn the dynamics of user-item interactions in recommender systems, accounting for the inherent differences between users and items. Empirical results on 5 public datasets demonstrate the superiority of CSINCERE over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict what people will like or want in the future. It does this by using two special spaces that can change over time, one for users and one for items. The model, called CSINCERE, doesn’t need labels to work, which makes it more useful than other methods. This is important because making labels can be hard and costly. The researchers tested their method on 5 real datasets and found it worked better than others. |
