Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Contextual Dependent Click Bandit Algorithm for Web Recommendation
Published in International computing and combinatorics conference (COCOON 2018), 2018
Weiwen Liu, Shuai Li, Shengyu Zhang
Field-aware Probabilistic Embedding Neural Network for CTR Prediction
Published in Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018), 2018
Weiwen Liu, Ruiming Tang, Jiajin Li, Jinkai Yu, Huifeng Guo, Xiuqiang He, Shengyu Zhang
Personalized Fairness-aware Re-ranking for Microlending
Published in Proceedings of the 13th ACM Conference on Recommender Systems (RecSys 2019), 2019
Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, Shengyu Zhang
Personalized Re-ranking with Item Relationships for E-commerce
Published in Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM 2020), 2020
Weiwen Liu, Qing Liu, Ruiming Tang, Junyang Chen, Xiuqiang He, Pheng Ann Heng
Item Relationship Graph Neural Networks for E-commerce
Published in IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2021), 2021
Weiwen Liu, Yin Zhang, Jianling Wang, Yun He, James Caverlee, Patrick PK Chan, Daniel S Yeung, Pheng-Ann Heng
Neural Re-ranking in Multi-Stage Recommender Systems: A Review
Published in The 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), 2022
Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, and Ruiming Tang.
Personalized Diversification for Neural Re-ranking in Recommendation
Published in IEEE 39th International Conference on Data Engineering (ICDE 2023), 2023
Weiwen Liu*, Yunjia Xi*, Jiarui Qin, Xinyi Dai, Ruiming Tang, Shuai Li, Weinan Zhang, Rui Zhang
Toolace: Winning the Points of LLM Function Calling
Published in The 13th International Conference on Learning Representations (ICLR 2025), 2025
Weiwen Liu*, Xu Huang*, Xingshan Zeng*, Xinlong Hao, Shuai Yu, Dexun Li, Shuai Wang et al.
talks
Neural Re-ranking for Multi-stage Recommender Systems
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Re-ranking is one of the most critical stages for multi-stage recommender systems (MRS), which re-orders the input ranking lists by modeling the cross-item interaction. Recent re-ranking methods have evolved into deep neural architectures due to the significant advances in deep learning. Neural re-ranking, therefore, has become a trending topic and many of the improved algorithms have demonstrated their use in industrial applications, enjoying great commercial success. The purpose of this tutorial is to explore some of the recent work on neural re-ranking, integrating them into a broader picture and paving ways for more comprehensive solutions for future research. In particular, we provide a taxonomy of current methods according to the objectives and training signals. We examine and compare these methods qualitatively and quantitatively, and identify some open challenges and future prospects.More information here
How Recommender Systems Can Benefit from Large Language Models: An Application Perspective
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Recommender systems (RS) play a crucial role in mitigating information overload and delivering personalized content that caters to users’ diverse needs. The emergence of large language models (LLMs) offers a new horizon for redefining recommender systems with vast general knowledge and reasoning capabilities. In this talk, we trace the technical evolution of recommender systems, highlighting key advancements from deep learning to LLMs and LLM agents.
User Behavior Modeling with Deep Learning for Recommendation: Recent Advances
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User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems. The exploration of key interactive patterns between users and items has yielded significant improvements and great commercial success across a variety of recommendation tasks. This tutorial aims to offer an in-depth exploration of this evolving research topic. We start by reviewing the research background of UBM, paving the way to a clearer understanding of the opportunities and challenges. Then, we present a systematic categorization of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. To provide an expansive understanding, we delve into each category, discussing representative models while highlighting their respective strengths and weaknesses. Furthermore, we elucidate on the industrial applications of UBM methods, aiming to provide insights into the practical value of existing UBM solutions. Finally, we identify some open challenges and future prospects in UBM. This comprehensive tutorial serves to provide a solid foundation for anyone looking to understand and implement UBM in their research or business. More information here