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Social Analytics Framework to Boost Recommendation in Online Learning Communities

机译：社会分析框架提高在线学习社区的推荐

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摘要

Online learning communities have become an important place serving informal learning due to the prevalence of online social networking services during the past few years. This paper proposes a social analytics framework aiming to boost recommendation service catering for the different learning demands of learners. Based on the traditional collaborative filtering approach, this study focuses on constructing topic-specific user credibility network by considering social relations and user behaviors. Both direct and indirect connections evidence from social analytics provide complementary information to construct user trust network. Regarding the topic-specific user credibility network, two features including influence and expertise are also computed to refine the credibility value between users. Furthermore, the performances of learners were further investigated in terms of longevity and centrality that could be referred when selecting suitable people for recommendation.
机译：在线学习社区已成为过去几年在线社交网络服务的普遍存在的重要地位。本文提出了一种社会分析框架，旨在提高建议服务迎合学习者的不同学习需求。基于传统的协作过滤方法，本研究侧重于考虑社会关系和用户行为来构建特定于主题的用户可信度网络。来自社会分析的直接和间接连接证据都提供了构建用户信任网络的互补信息。关于特定于主题的用户可信网络，还计算了两个包括影响和专业知识的功能以优化用户之间的信誉值。此外，在选择合适的人士时，可以在寿命和中心的寿命和中心进行进一步调查学习者的表演。

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