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社会化推荐系统

Recommender Systems with Social Regularization
课程网址: http://videolectures.net/wsdm2011_lyu_rss/  
主讲教师: Michael R. Lyu
开课单位: 耶鲁大学
开课时间: 2011-08-09
课程语种: 英语
中文简介:
尽管在过去十年中对推荐系统进行了全面分析,但社交推荐系统的研究才刚刚开始。本文旨在通过结合社会网络信息提供一种改进推荐系统的通用方法,提出一种具有社会正规化的矩阵分解框架。本文的贡献有四个方面:(1)我们详细阐述了社交网络信息如何使推荐系统受益; (2)我们解释了基于社交的推荐系统和信任感知推荐系统之间的差异; (3)我们用社会正规化这个术语来表示对推荐系统的社会约束,并系统地说明了如何用社会正规化设计矩阵分解目标函数; (4)所提出的方法非常通用,可以很容易地扩展到包含其他上下文信息,如社交标签等。对两个大型数据集的实证分析表明,我们的方法优于其他最先进的方法。
课程简介: Although Recommender Systems have been comprehensively analysed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.
关 键 词: 全面分析; 推荐系统; 目标函数
课程来源: 视频讲座网
最后编审: 2020-09-27:yumf
阅读次数: 149