Diverging towards the common good: heterogeneous self-organisation in decentralised recommenders
Diverging towards the common good: heterogeneous self-organisation in decentralised recommenders
复制标题
走向共同利益：去中心化推荐系统中的异构自组织
DOI：
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发表时间：
2012
期刊：
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通讯作者：
François Taïani
中科院分区：
文献类型：
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作者：
Anne;François Taïani
去中心化的社交网络承诺提供高度个性化的、隐私保护的、可扩展的和鲁棒的关键社交网络功能的实现，例如搜索、查询扩展和推荐。这样的系统通过利用隐含的社会联系来实现个性化服务，从而超越了传统的在线社交网络。然而，当前分散的社会系统通常统一对待所有用户，而不同的用户子社区实际上可能在不同的机制下工作得最好。在本文中，我们着眼于分散的社交网络寻求集群用户表现出类似的行为，以提供分散的建议的具体情况。这些去中心化的推荐系统通常依赖于统一应用于所有用户的单一指标来提取相似性，而没有这种一刀切的方法似乎是很自然的。更具体地说，我们在本文中显示，使用一个真实的Twitter的痕迹，（一）个人用户可以受益于个性化的策略的背景下，分散的推荐系统，以及（二）整体系统性能得到改善时，系统帐户的不同需求的用户，即当每个用户被允许发散，并使用其最佳策略。
Decentralised social networks promise to deliver highly personalised, privacy-preserving, scalable and robust implementations of key social network features, such as search, query extensions, and recommendations. Such systems go beyond traditional online social networks by leveraging implicit social ties to implement personalised services. Yet, current decentralised social systems usually treat all users uniformly, when different sub-communities of users might in fact work best with different mechanisms. In this paper, we look at the specific case of decentralised social networks seeking to cluster users exhibiting similar behaviours to provide decentralised recommendations. These decentralised recommendation systems typically rely on a single metric applied uniformly to all users to extract similarities, while it seems natural that there is no such one-size-fits-all approach. More specifically we show in this paper, using a real Twitter trace, that (i) individual users can benefit from a personalised strategy in the context of decentralised recommendation systems, and that (ii) overall system performance is improved when the system accounts for the varying needs of its users i.e. when each user is allowed to diverge and use its optimal strategy.