Summary of Breaking Determinism: Fuzzy Modeling Of Sequential Recommendation Using Discrete State Space Diffusion Model, by Wenjia Xie et al.
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model
by Wenjia Xie, Hao Wang, Luankang Zhang, Rui Zhou, Defu Lian, Enhong Chen
First submitted to arxiv on: 31 Oct 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 The paper revisits sequential recommendation (SR) from an information-theoretic perspective, recognizing the limitations of conventional sequential modeling methods. These methods fail to capture the randomness and unpredictability of user behavior. To overcome these limitations, the authors introduce the DDSR model, which uses fuzzy sets of interaction sequences to better capture the evolution of users’ real interests. The model is formally based on diffusion transition processes in discrete state spaces, unlike common diffusion models that operate in continuous domains. Additionally, the authors use semantic labels derived from quantization or RQ-VAE to replace item IDs, enhancing efficiency and improving cold start issues. Testing on three public benchmark datasets shows that DDSR outperforms existing state-of-the-art methods in various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can do a better job of predicting what people will like based on their past behavior. Right now, most ways of doing this are not very good because they don’t take into account the fact that people’s tastes and preferences can change suddenly. The authors of the paper introduce a new way of thinking about this problem that uses “fuzzy sets” to capture these changes. They also come up with a new kind of model called DDSR that is better at handling this type of data than other models are. When they test their approach on three different datasets, it turns out to be really effective and does a lot better than the other approaches do. |
Keywords
» Artificial intelligence » Diffusion » Quantization
