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作 者:黄晓雯[1] 严明[1] 桑基韬[1] 徐常胜[1,2]
机构地区:[1]中国科学院自动化研究所模式识别国家重点实验室,北京100190 [2]中国-新加坡数字媒体研究院,新加坡119615
出 处:《计算机科学》2016年第7期51-56,共6页Computer Science
摘 要:随着社交媒体的兴起,各种社交媒体服务应运而生,社交媒体多源化现象越来越明显。一种基于关联规则挖掘的方法可以用来分析研究社交媒体多源现象,即通过同一个用户与不同社交媒体上多源数据的行为交互,挖掘社交媒体多源数据知识关联,进而设计跨网络协同的视频推荐应用。本研究框架主要分为3个步骤:(1)基于主题建模的知识发现,对用户和视频进行主题建模,得到其在主题层上的表示;(2)基于关联规则挖掘的跨网络知识关联,以跨网络共同用户作为连接不同网络的桥梁,利用关联规则的方法挖掘不同网络间的知识关联;(3)基于跨网络知识发现的冷启动视频推荐,将用户和视频映射到同一主题空间并进行主题匹配,最终进行视频推荐。实验结果表明,通过跨网络用户协同,该跨网络知识关联方法能得到除了语义关联外更加灵活有效的跨网络关联,并在冷启动的跨网络视频推荐中取得较好的推荐效果。Nowadays, with the rise of social media, various and disparate social media services spring up like mushrooms. As a result, the social media variety phenomenon has become more and more pervasive. In this paper, we proposed a novel association rule-based method to investigate into this social media variety phenomenon, which aims to mine the cross-network knowledge association by leveraging the collective intelligence of plenty of cross-network overlapped users. A cold-start video recommendation application was further designed based on the derived cross-network knowledge association. Three stages are mainly involved in the framework. (1)heterogeneous topic modeling, where YouTube videos and Twitter users are modeled in topic level; (2)association rule-based knowledge association, where overlapped users serve as bridge between different social media networks and a novel association rule-based method is used to derive the topic correlation between different networks; (3)cold-start video recommendation, where the Twitter users and YouTube videos are transferred to the same topic space and matched on topic level. The experiments on a real- world dataset demonstrate the effectiveness of the proposed association method, which is able to capture some more flexible knowledge association beyond the semantic association. Moreover, the performance of the cold-start video re- commendation application is also very promising.
分 类 号:TP399[自动化与计算机技术—计算机应用技术]
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