CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems

被引：100

作者：

Lian, Jianxun ^{[1
]}

Zhang, Fuzheng ^{[2
]}

Xie, Xing ^{[2
]}

Sun, Guangzhong ^{[1
]}

机构：

[1] Univ Sci & Technol China, Hefei, Peoples R China

[2] Microsoft Res, Redmond, WA USA

来源：

WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB | 2017年

关键词：

recommendation system; cross domain; neural network;

D O I：

10.1145/3041021.3054207

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through this neural network embedding the framework can be further extended by advanced deep learning techniques.

引用

页码：817 / 818

页数：2