An Unified Search and Recommendation Foundation Model for Cold-Start Scenario

no code implementations • 16 Sep 2023 • Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, Guannan Zhang

With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks.

Recommendation Systems Transfer Learning

Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations

no code implementations • 23 Nov 2022 • Ningning Li, Qunwei Li, Xichen Ding, Shaohu Chen, Wenliang Zhong

First, a user has multiple embeddings to reflect various interests, and such number is fixed.

Contrastive Learning Recommendation Systems

Device-Cloud Collaborative Recommendation via Meta Controller

no code implementations • 7 Jul 2022 • Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, Hongxia Yang

To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller.

counterfactual Device-Cloud Collaboration

Infer Implicit Contexts in Real-time Online-to-Offline Recommendation

1 code implementation • 8 Jul 2019 • Xichen Ding, Jie Tang, Tracy Liu, Cheng Xu, Yaping Zhang, Feng Shi, Qixia Jiang, Dan Shen

Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei.