no code implementations • NAACL 2019 • Guang-Neng Hu
We learn such embeddings of users, items, and words jointly, and predict user preferences on items based on these learned representations.
no code implementations • 22 Jan 2019 • Guang-Neng Hu, Yu Zhang, Qiang Yang
Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods.
1 code implementation • 18 Apr 2018 • Guang-Neng Hu, Yu Zhang, Qiang Yang
CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa.
no code implementations • 26 Mar 2018 • Guang-Neng Hu, Xin-yu Dai, Feng-Yu Qiu, Rui Xia, Tao Li, Shu-Jian Huang, Jia-Jun Chen
First, we propose a novel model {\em \mbox{MR3}} to jointly model three sources of information (i. e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics.
no code implementations • 31 Jan 2017 • Guang-Neng Hu, Xin-yu Dai
On top of text features we uncover the review dimensions that explain the variation in users' feedback and these review factors represent a prior preference of users.
no code implementations • 11 Jan 2016 • Guang-Neng Hu, Xin-yu Dai, Yunya Song, Shu-Jian Huang, Jia-Jun Chen
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices.