.png){kind=icon}
返回
Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.08.129.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Implicit feedback collaborative filtering has attracted a lot of attention in collaborative filtering, which is called one-class collaborative filtering (OCCF). However, the low recommendation accuracy and the high cost of previous methods impede its generalization in real scenarios. In this paper, we develop a new model named pairwise probabilistic matrix factorization (PPMF) by using the advantages of RankRLS. PPMF model takes RankRLS integrated with PMF (probabilistic matrix factorization) to learn the relative preference for items. Different from previous works, PPMF minimizes the average number of inversions in ranking rather than maximize the gaps of the binary predicted values for OCCF problem. Meanwhile, we propose to optimize the PPMF model by the pointwise stochastic gradient descent algorithm based on bootstrap sampling, which is more effective for parameter learning than the original optimization method used in previous works. Experiments on two datasets show that PPMF model achieves satisfactory performance and outperforms the state-of-the-art implicit feedback collaborative ranking models by using different evaluation metrics. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Recommended systems
Collaborative filtering
Collaborative ranking
Implicit feedback
Probabilistic matrix factorization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Seismic anisotropy indicates organized melt beneath the Mid-Atlantic Ridge aids seafloor spreading.png)
Geology
OAAI
GeologyIF0
OAAI分享收藏
Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
分享收藏Study on Preparation and Characterization of the SiO<sub>2</sub> Photonic CrystalSiO₂光子晶体的制备与表征研究
PREAI
分享收藏
查看更多内容