Awesome Recommend-System Pretraining Papers
Overview
The "awesome-recommend-system-pretraining-papers" project is a comprehensive collection of academic resources focused on pre-trained models for recommendation systems. This project not only includes a wide array of research papers but also explores adjacent fields like the application of large language models in recommendation systems. This curated list serves as an invaluable asset for researchers, practitioners, and students interested in the latest advancements and trends in recommendation technologies.
Key Themes
Pre-trained Models in Recommendation Systems: The project emphasizes how pre-trained models are revolutionizing recommendation systems by enhancing performance and adaptability. These models are pivotal in personalizing user experience by predicting user preferences based on historical data.
Large Language Models: It delves into the intersection of recommendation systems and large language models, examining how powerful language processing abilities can enhance recommendation systems. Papers under this theme discuss the adaptation and potential of large language models in offering more nuanced and complex recommendations.
Contribution Invitation
The project invites the community to participate through issues or pull requests, demonstrating its commitment to community-driven growth and continuous improvement. It’s an open invitation for scholars and developers to contribute and expand this repository, making it a dynamic and collaborative platform.
Collaboration Opportunity
A notable feature is the collaboration offer with the Huawei Noah Ark Recommendation & Search Lab. This lab is actively working on machine learning and data mining technologies related to recommendation and search systems, providing opportunities for graduates and interns to engage in cutting-edge research under the guidance of a leading tech organization.
Structured Content
The repository categorizes its paper list into specific areas such as:
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Review Articles: Papers summarizing existing knowledge and outlining future directions.
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Datasets: A list of datasets that facilitate research and experimentation in recommendation systems.
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Empirical Studies: Research focusing on practical implementations and findings in recommendation systems.
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Sequential/Session-Based Recommendation: Techniques that predict user needs based on their sequential actions or session-based interactions.
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Pretraining Techniques: Exploration of different pretraining methods such as user representation, two-tower pretraining, and graph pretraining.
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Prominent Applications: Use of language models as recommendation tools and their methodologies like prompt learning, examining their efficacy and limitations.
Additional Resources
The project also highlights workshops and tutorials, offering practical guidance and an opportunity for the community to engage with the material more interactively. Additionally, links to related hubs and repositories broaden the scope for those seeking extensive knowledge in similar domains.
Conclusion
Curated by Xiangyang Li from Peking University, this GitHub repository stands as a testament to the evolving field of recommendation system models. It not only serves as a knowledge hub but also as a platform for innovation and collaboration, propelled by the contributions of its diverse and engaged community.
Whether you are a seasoned researcher or a fresh enthusiast in the realm of recommendation systems, this repository offers a wealth of information and opportunities to deepen your understanding and contribute to impactful advancements.