Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu
Abstract
Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index. By indexing items in a tree hierarchy and training a user-node preference prediction model satisfying a max-heap like property in the tree, TDM provides logarithmic computational complexity w.r.t. the corpus size, enabling the use of arbitrary advanced models in candidate retrieval and recommendation.
BibTeX
@inproceedings{NEURIPS2019_1c6a0198,
author = {Zhu, Han and Chang, Daqing and Xu, Ziru and Zhang, Pengye and Li, Xiang and He, Jie and Li, Han and Xu, Jian and Gai, Kun},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Joint Optimization of Tree-based Index and Deep Model for Recommender Systems},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/1c6a0198177bfcc9bd93f6aab94aad3c-Paper.pdf},
volume = {32},
year = {2019}
}