194 citations · 351 across the 7 of their papers we have counts for
7 papers
Path-based Deep Network for Candidate Item Matching in Recommenders
Houyi Li, Zhihong Chen, Chenliang Li +5
The large-scale recommender system mainly consists of two stages: matching and ranking. The matching stage (also known as the retrieval step) identifies a small fraction of relevan…
Learning a Product Relevance Model from Click-Through Data in E-Commerce
Shaowei Yao, Jiwei Tan, Xi Chen +4
The search engine plays a fundamental role in online e-commerce systems, to help users find the products they want from the massive product collections. Relevance is an essential r…
Explanation as a Defense of Recommendation
Aobo Yang, Nan Wang, Hongbo Deng +1
Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanatio…
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
Cheng Zhao, Chenliang Li, Rong Xiao +2
In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interacti…
ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
Zhihong Chen, Rong Xiao, Chenliang Li +3
Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and…
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Daoyuan Chen, Yaliang Li, Minghui Qiu +7
Large pre-trained language models such as BERT have shown their effectiveness in various natural language processing tasks. However, the huge parameter size makes them difficult to…