Summary of Generative Diffusion Models For Sequential Recommendations, by Sharare Zolghadr et al.
Generative Diffusion Models for Sequential Recommendations
by Sharare Zolghadr, Ole Winther, Paul Jeha
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach leveraging diffusion models is introduced to address challenges in sequential recommendation tasks faced by generative models like VAEs and GANs. The proposed method represents item embeddings as distributions rather than fixed vectors, allowing for a more adaptive reflection of users’ diverse interests. The model converts the target item embedding into a Gaussian distribution during the diffusion phase, facilitating the representation of sequential item distributions and the injection of uncertainty. An Approximator then processes this noisy item representation to reconstruct the target item. In the reverse phase, the model utilizes users’ past interactions to reverse the noise and finalize the item prediction through a rounding operation. The research enhances the DiffuRec architecture by adding offset noise in the diffusion process and incorporating a cross-attention mechanism in the Approximator. These contributions led to the development of a new model, DiffuRecSys, which improves performance on three public benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new approach to sequential recommendation tasks using diffusion models. The goal is to improve upon existing methods like VAEs and GANs by representing item embeddings as distributions rather than fixed vectors. This allows for a more adaptive reflection of users’ diverse interests and various item aspects. The model works by converting the target item embedding into a Gaussian distribution during the diffusion phase, then processing this noisy item representation to reconstruct the target item. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Embedding
