Recommender Transformers with Behavior Pathways

no code implementations • 13 Jun 2022 • Zhiyu Yao, Xinyang Chen, Sinan Wang, Qinyan Dai, Yumeng Li, Tanchao Zhu, Mingsheng Long

We conclude this characteristic for sequential behaviors of each user as the Behavior Pathway.

Sequential Recommendation

MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

1 code implementation • 25 Feb 2022 • Linhao Luo, Yumeng Li, Buyu Gao, Shuai Tang, Sinan Wang, Jiancheng Li, Tanchao Zhu, Jiancai Liu, Zhao Li, Shirui Pan

We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.

A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction

no code implementations • 21 May 2021 • Ze Meng, Jinnian Zhang, Yumeng Li, Jiancheng Li, Tanchao Zhu, Lifeng Sun

Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which is one of the most typical machine learning tasks in personalized advertising and recommender systems.

Click-Through Rate Prediction Neural Architecture Search +1

Learning User Representations with Hypercuboids for Recommender Systems

3 code implementations • 11 Nov 2020 • Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, Wenwu Ou

Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests.

Collaborative Filtering Recommendation Systems