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个性化标签推荐的成对交互张量分解

Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation
课程网址: http://videolectures.net/wsdm2010_rendle_pit/  
主讲教师: Steffen Rendle
开课单位: 谷歌公司
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
标签在最近的许多网站中扮演着重要的角色。推荐者系统可以帮助向用户推荐他可能想要为某一特定项目标记的标记。基于Tucker分解(TD)模型的分解模型提供了高质量的标签推荐,其性能优于PageRank、FolkRank、协同过滤等其他方法。TD模型的问题在于,分解模型的立方核心张量导致在分解维度上有一个立方运行时,用于预测和学习。本文提出了分解模型PITF(两两相互作用张量分解),它是TD模型在线性运行时下学习和预测的一个特例。PITF显式地为用户、项目和标记之间的成对交互建模。该模型采用贝叶斯个性化排名(BPR)标准进行学习,BPR标准最初是用于项目推荐的。实证结果表明,该模型在运行时的预测性能优于TD模型,甚至可以获得更好的预测质量。除了我们的实验室实验,PITF还赢得了ECML/PKDD发现挑战赛2009基于图形的标签推荐。
课程简介: Tagging plays an important role in many recent websites. Recommender systems can help to suggest a user the tags he might want to use for tagging a specific item. Factorization models based on the Tucker Decomposition (TD) model have been shown to provide high quality tag recommendations outperforming other approaches like PageRank, FolkRank, collaborative filtering, etc. The problem with TD models is the cubic core tensor resulting in a cubic runtime in the factorization dimension for prediction and learning. In this paper, we present the factorization model PITF (Pairwise Interaction Tensor Factorization) which is a special case of the TD model with linear runtime both for learning and prediction. PITF explicitly models the pairwise interactions between users, items and tags. The model is learned with an adaption of the Bayesian personalized ranking (BPR) criterion which originally has been introduced for item recommendation. Empirically, we show on real world datasets that this model outperforms TD largely in run- time and even can achieve better prediction quality. Besides our lab experiments, PITF has also won the ECML/PKDD Discovery Challenge 2009 for graph-based tag recommendation.
关 键 词: 计算机科学; 语义网; 注释
课程来源: 视频讲座网
入库时间: 2018-04-23
最后编审: 2019-10-29:cwx
阅读次数: 135