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基于共同用户行为分析的跨社交媒体网络应用研究
严明
学位类型工学博士
导师徐常胜
2016-05-28
学位授予单位中国科学院大学
学位授予地点北京
学位专业模式识别与智能系统
关键词跨网络用户行为分析 跨网络协同应用 用户建模
摘要随着社交媒体的兴起与多种社交媒体服务的产生,人们如今往往同时参与多个不同的社交媒体网络以满足多样性的用户需求,跨网络共同用户参与现象已越来越普遍。同时,为了进一步提升用户体验,并吸引更多的活跃用户,各大互联网公司间的跨网络合作也越来越频繁,跨网络协作正在逐步成为未来互联网发展的主流趋势。这些都为跨网络共同用户行为分析提供了充分的数据基础,并为设计跨网络协同应用提供了良好的环境。
相对于传统的单一网络,跨网络分析与应用涉及到更加海量的碎片化息,这些碎片化的数据具有异构性、平台无关性、时序动态性等特点,这些复杂的数据特性给跨网络信息的整合与利用带来了巨大的挑战。如何从这些分散于不同网络平台的碎片化数据中提取有价值的信息,进而全方位地分析和理解用户的行为与兴趣模式,从而设计有效的应用服务,是极具商业前景与研究价值的。跨社交媒体网络的应用研究一方面有助于加深对用户的全方位理解,以进行跨网络的用户建模,有效地解决单一网络数据稀疏性和冷启动等问题;另一方面,有利于理解不同网络的独特特性,进行不同网络间的优势互补,引领全新的跨网络协作的商业模式。因此,本文针对跨网络用户数据的复杂特性,围绕共同用户为中心,以跨网络用户行为分析为基础,跨网络协同应用为最终目的,并从共同用户行为模式分析、跨网络数据整合与跨网络用户兴趣整合三方面开展了以下几方面的工作:
1. 提出了从用户个体粒度层直接分析共同用户在不同社交媒体网络的微观行为模式。跨网络语境下,用户行为分散在不同的网络平台,同一用户在不同网络的行为具有一定的一致性和差异性,分析用户的跨网络特性能够更好地理解用户兴趣和行为模式。因此,本文分别从同一用户在不同网络的社交行为一致性与不同网络的时序性两方面进行共同用户跨网络分析。其中,社交行为一致性分析侧重分析同一用户在不同网络的社交关系是否具有一致性,何种社交关系在不同网络更易于保持一致性以及社交关系与特定的用户行为间是否具有协同性。跨网络时序性分析分别从全局、用户个体以及主题层三个不同粒度分析和比较了共同用户在不同网络关注同一事件的时间先后顺序。
2. 提出了一种根据共同用户在不同社交媒体网络的协同行为来关联不同网络知识,进行跨网络数据整合的方法。为了获取不同网络上异构的数据知识,首先在不同网络分别进行主题建模得到数据的泛化表示,然后利用跨网络共同用户来桥接不同网络的知识表示,根据用户在不同网络空间主题分布的一一对应关系,最终学习不同网络间的分布映射函数以进行跨网络数据关联。由于该关联方法以大量跨网络共同用户作为高层的监督与感知,使求得的关联能突破语义关联的限制,在更细的粒度下进行感知。基于该数据关联,最终设计了一种跨网络视频推广应用,通过借用Twitter网络的信息传播高效性来帮助快速地推广特定的YouTube视频。
3. 提出了一种基于跨网络用户行为协同的统一视频推荐解决框架,同时处理推荐系统中的新用户、冷启动与数据稀疏性问题。该方法首先学习一个转移矩阵来关联不同网络上的用户模型,使用户在Twitter上的社交兴趣可以直接被用来预测其大致的YouTube视频偏好。然后针对不同网络用户兴趣可能存在的不一致性问题,进一步设计了一种自适应的权重融合策略来平衡不同网络用户模型对推荐性能的影响。实验结果表明,通过融入辅助网络的信息并设计跨网络协同的解决方法,不仅在推荐准确性上有一定的性能提升,同时也能有效地提升推荐多样性和新颖性。
4. 提出了一种基于动态跨网络关联的冷启动视频推荐解决方案,通过理解不同网络上用户动态的兴趣与行为模式,来得到相对稳定的跨网络用户兴趣关联模式,进行有效地跨网络用户建模。为了捕捉短期的用户兴趣,本工作将整个时间空间分成了许多相同时间长度的小的时间段区间,并在每个小的时间段区间上分别进行动态的用户建模,然后利用学得的用户兴趣表示在每个时间段内进行跨网络的用户兴趣关联。同时,利用连续时间段区间的关联平稳性特性,不断地在时间段区间之间增量式地更新学得的跨网络关联,使发现的关联模式更具时效性。
其他摘要
With the emergence and rise of social media, people now usually engage in multiple online social networks simultaneously to serve for their diverse information needs. The cross-network user participation phenomenon is becoming more and more frequent. Meanwhile, to better improve user experience and increase user adoption, more and more internet companies also begin to release cross-network features and seek for strategic corporation, cross-network collaboration is gradually becoming the future trend of internet development. These all provide huge possibility for cross-network user behavior analysis and lay foundation for the cross-network collaborative application.
 
Compared with traditional single-network situation, cross-network analysis and application involves with a large amount of scattered information. These scattered information has some characteristics, such as heterogeneity, network gap and temporal dynamics, which make it a big challenge to conduct cross-network data integration and exploitation. How to extract valuable knowledge from these information for comprehensive user understanding and further design effective cross-network collaborative application is very important and promising. On one hand, the cross-network user analysis and application can help better understand the typical user interest from different perspectives and effectively address the cold start and data sparsity problems under single-network circumstance; On the other hand, it can also facilitate the understanding of unique network characteristics, so as to complement respective advantages between different networks and lead to brand new business model. Therefore, in this work, based on the cross-network user behavior analysis and aiming at designing novel collaborative application, we have conducted the following research work on three different aspects of common user behavior pattern analysis, cross-network data integration and cross-network user interest integration:
1. We propose to directly analyze the micro user behavior patterns in different social media networks from user level. Under cross-network circumstance, the user behaviors usually distribute on different networks, and the user behaviors of the same user in different networks have some sort of consistency and difference. Analyzing these characteristics can better understand the typical user interest and behavior patterns. Therefore, we conduct cross-network user analysis on two aspects, i.e., social behavior consistency and cross-network temporal dynamics. Social behavior consistency focuses on analyzing whether the same user's social links in different networks have some sort of consistency and whether the formation of a user's typical social links has certain relation with his/her social behaviors.  While cross-network temporal dynamics focuses on analyzing and comparing the reaction speed to the same event in different networks from three different levels, i.e., global level, user level and category level.
 
2. We propose a method to associate the heterogeneous knowledge in different networks by leveraging the collaborative behaviors of the common users in different networks. To represent the heterogeneous knowledge in different networks, we first conduct some typical topic modeling approaches in respective networks. Then we use the common users in different networks to connect the derived network spaces. With the same users' topical representations as a high-level supervision, we finally learn the distribution transfer function for cross-network data association. Based on the derived data association, we further design a cross-network YouTube video promotion application, by leveraging the high propagation efficiency of the Twitter network.
 
3. We propose a unified YouTube video recommendation framework via cross-network user collaboration, to address the typical new user, cold start and data sparsity problems in recommender systems. The proposed method first learns a transfer matrix, based on which users' Twitter social behaviors can be directly used to estimate their video preference on YouTube. To deal with the inconsistency of different user models on Twitter and YouTube, we further design an adaptive fusion strategy to balance their contributions on the final recommendation performance. Experimental results show that the proposed solution achieves superior performance on three kinds of typical users, in term of not only accuracy, but also diversity and novelty.
 
4. We propose a dynami?c association framework to associate different networks, by observing users’ temporal behaviors on them. A cold-start video recommendation solution is further designed based on the derived association. To capture the stable short-term user interest, we split the whole time space into many time sessions with equal length, and conduct the dynamic user modeling within each time session, then the derived user interest representations in each session are further used to compute the cross-network user interest association. Besides, assuming that the association patterns will not change dramatically in successive time sessions, we also incrementally update the derived association with time session by session.
 
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/11617
专题毕业生_博士学位论文
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
严明. 基于共同用户行为分析的跨社交媒体网络应用研究[D]. 北京. 中国科学院大学,2016.
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