Clustered Embedding Learning for Recommender Systems

no code implementations • 3 Feb 2023 • Yizhou Chen, Guangda Huzhang, AnXiang Zeng, Qingtao Yu, Hui Sun, Heng-yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou

However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up.

Recommendation Systems

Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce

no code implementations • 16 Dec 2020 • Yuting Chen, Yanshi Wang, Yabo Ni, An-Xiang Zeng, Lanfen Lin

Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network.

Cultural Vocal Bursts Intensity Prediction Recommendation Systems

Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks

no code implementations • 28 May 2018 • Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, An-Xiang Zeng, Luo Si

In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.