Summary of Actions Speak Louder Than Words: Trillion-parameter Sequential Transducers For Generative Recommendations, by Jiaqi Zhai et al.
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
by Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, Yinghai Lu, Yu Shi
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 Large-scale recommendation systems rely heavily on high-cardinality, heterogeneous features and massive amounts of user actions. Despite training on vast datasets with thousands of features, most Deep Learning Recommendation Models (DLRMs) in the industry struggle to scale with compute resources. This paper aims to address this challenge by [insert key method/technique here], enabling more efficient and effective recommendation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recommendation systems are super important because they help us find what we want online, like movies or products. Right now, these systems use a lot of information about users and the things they do, but they’re not very good at handling all that data when there’s so much of it. This is a problem for companies trying to make recommendation systems better. The paper tries to solve this problem by finding a new way to use machine learning models, making them more efficient and helping them work with huge amounts of data. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
