Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation

SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020, pp. 319-328, 2020.

Cited by: 0|Bibtex|Views180|DOI:https://doi.org/10.1145/3397271.3401119
EI
Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

Modeling large scale and rare-interaction users are the two major challenges in recommender systems, which derives big gaps between researches and applications. Facing to millions or even billions of users, it is hard to store and leverage personalized preferences with a user embedding matrix in real scenarios. And many researches pay att...More

Code:

Data:

Your rating :
0

 

Tags
Comments