CASIA OpenIR  > 智能感知与计算研究中心
Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback
Guo, Weiyu; Wu, Shu; Wang, Liang; Tan, Tieniu
2015
Conference NameIEEE/WIC/ACM International Conference on Web Intelligence (WI)
Source PublicationIn Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2015
Conference DateDecember 6-9
Conference PlaceSingapore
Abstract
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. 
KeywordPersonalized Ranking Adaptive Sampling Pairwise Learning
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12342
Collection智能感知与计算研究中心
Corresponding AuthorWu, Shu
Recommended Citation
GB/T 7714
Guo, Weiyu,Wu, Shu,Wang, Liang,et al. Adaptive Pairwise Learning for Personalized Ranking with Content and Implicit Feedback[C],2015.
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