Summary of Sigma: Selective Gated Mamba For Sequential Recommendation, by Ziwei Liu et al.
SIGMA: Selective Gated Mamba for Sequential Recommendation
by Ziwei Liu, Qidong Liu, Yejing Wang, Wanyu Wang, Pengyue Jia, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper presents a novel approach to Sequential Recommender Systems (SRS) that leverages the transformer-based architecture Mamba to improve efficiency and accuracy. While Mamba has shown exceptional performance in time series prediction, integrating it into SRS poses challenges due to its unidirectional nature and instability in state estimation. To address these limitations, this study proposes a new approach that combines Mamba with other techniques to capture the full context of user-item interactions and detect short-term patterns within interaction sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better recommendations on what to watch or buy next by using a special kind of computer model called Mamba. Right now, these models are not very efficient because they process lots of data at once. The people working on this problem want to find ways to make the models work faster and more accurately. They know that Mamba is good at predicting what will happen in the future, but it has some problems too. It can only look one way and doesn’t do well with short-term patterns. So, they are trying to figure out how to use Mamba in a better way to make really good recommendations. |
Keywords
» Artificial intelligence » Time series » Transformer
