中文版 | English

在结果中检索

CASIA OpenIR
（本次检索基于用户作品认领结果）

浏览/检索结果: 共6条，第1-6条 帮助

限定条件            
已选(0)清除 条数/页：   排序方式：
Learning to Hash for Recommendation with Tensor Data 会议论文
, Guangzhou, China, 2015-09-18
作者:  Yin, Qiyue;  Wu, Shu;  Wang, Liang
浏览  |  Adobe PDF(355Kb)  |  收藏  |  浏览/下载：225/93  |  提交时间：2017/02/27
Multiple Attribute Aware Personalized Ranking 会议论文
Proc. Asia Pacific Web Conference, Guangzhou, China, 2015
作者:  Weiyu Guo;  Shu Wu;  Liang Wang;  Tieniu Tan
Adobe PDF(504Kb)  |  收藏  |  浏览/下载：230/105  |  提交时间：2017/02/25
Collaborative Prediction for Multi-entity Interaction With Hierarchical Representation 会议论文
In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015, Melbourne, Oct 24-28
作者:  Liu, Qiang;  Wu, Shu;  Wang, Liang
浏览  |  Adobe PDF(1709Kb)  |  收藏  |  浏览/下载：318/112  |  提交时间：2016/10/24
Collaborative Prediction  Factorization Model  Hierarchical Representation  
Personalized Semantic Ranking for Collaborative Recommendation 会议论文
In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2015, Santiago, August 9-13
作者:  Xu, Song;  Wu, Shu;  Wang, Liang
浏览  |  Adobe PDF(969Kb)  |  收藏  |  浏览/下载：177/88  |  提交时间：2016/10/24
Learning To Rank  Recommendation  User-generated Content  
Social-Relational Topic Model for Social Networks 会议论文
In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015, Melbourne, Oct 24-28
作者:  Guo, Weiyu;  Wu, Shu;  Wang, Liang;  Tan, Tieniu
Adobe PDF(1147Kb)  |  收藏  |  浏览/下载：354/119  |  提交时间：2016/10/24
Topic Modeling  Social Networks  Social Link Generation  
Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback 会议论文
In Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2015, Singapore, December 6-9
作者:  Guo, Weiyu;  Wu, Shu;  Wang, Liang;  Tan, Tieniu
Adobe PDF(361Kb)  |  收藏  |  浏览/下载：332/117  |  提交时间：2016/10/24
Personalized Ranking  Adaptive Sampling  Pairwise Learning