Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback | |
Guo, Weiyu; Wu, Shu; Wang, Liang; Tan, Tieniu | |
2015 | |
会议日期 | December 6-9 |
会议地点 | Singapore |
关键词 | Personalized Ranking Adaptive Sampling Pairwise Learning |
英文摘要 |
Pairwise learning algorithms are a vital technique for personalized ranking with implicit feedback. They usually assume that each user is more interested in items which have been selected by the user than remaining ones. This pairwise assumption usually derives massive training pairs. To deal with such large-scale training data, the learning algorithms are usually based on stochastic gradient descent with uniformly drawn pairs. However, the uniformly sampling strategy often results in slow convergence. In this paper, we first uncover the reasons of slow convergence. Then, we associate contents of entities with
characteristics of data sets to develop an adaptive item sampler for drawing informative training data. In this end, to devise a robust personalized ranking method, we accordingly embed our sampler into Bayesian Personalized Ranking (BPR) framework, and further propose a Content-aware and Adaptive Bayesian Personalized Ranking (CA-BPR) method, which can deal with both contents and implicit feedbacks in a unified learning process. The experimental results show that, our adaptive item sampler has more potential to speed up BPR learning and CA-BPR definitively outperforms the state-of-the-art methods in personalized ranking. |
会议录 | In Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2015 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/12342] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Wu, Shu |
推荐引用方式 GB/T 7714 | Guo, Weiyu,Wu, Shu,Wang, Liang,et al. Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback[C]. 见:. Singapore. December 6-9. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论