论文标题
普遍社交网络的个性化推荐系统
A Personalized Recommender System for Pervasive Social Networks
论文作者
论文摘要
当前相互连接的便携式设备的可用性以及Web 2.0的出现，提出了支持任何地方以及任何时间访问移动用户生成和共享的大量内容的问题。在这项工作中，我们为普遍的社交网络（称为Pervasive Pliers（Ppliers））提出了一个新颖的框架，能够以高度个性化的方式发现和选择单个移动用户感兴趣的内容。 Ppliers利用最近提出的基于钳子标签的推荐系统作为上下文，一种推理工具，能够将建议调整为不同用户的异质兴趣配置文件。当维持有关网络的有限知识时，Ppliers也有效地运行。它是在完全分散的环境中实现的，在该环境中，新内容是通过网络连续生成和扩散的，并且仅依赖于在接近性触点期间以及通过设备通信的过程中交换单节点知识的交换。我们通过在三种不同的情况下模拟其行为来评估Ppliers：大型活动（Expo 2015），会议场地（ACM KDD 2015）和赫尔辛基市的工作日。对于每种情况，我们都使用了真实或合成的移动性跟踪，并从Twitter交互中提取了真实数据集来表征用户内容的生成和共享。
The current availability of interconnected portable devices, and the advent of the Web 2.0, raise the problem of supporting anywhere and anytime access to a huge amount of content, generated and shared by mobile users. In this work we propose a novel framework for pervasive social networks, called Pervasive PLIERS (pPLIERS), able to discover and select, in a highly personalized way, contents of interest for single mobile users. pPLIERS exploits the recently proposed PLIERS tag based recommender system as context a reasoning tool able to adapt recommendations to heterogeneous interest profiles of different users. pPLIERS effectively operates also when limited knowledge about the network is maintained. It is implemented in a completely decentralized environment, in which new contents are continuously generated and diffused through the network, and it relies only on the exchange of single nodes knowledge during proximity contacts and through device to device communications. We evaluated pPLIERS by simulating its behaviour in three different scenarios: a big event (Expo 2015), a conference venue (ACM KDD 2015), and a working day in the city of Helsinki. For each scenario, we used real or synthetic mobility traces and we extracted real datasets from Twitter interactions to characterise the generation and sharing of user contents.
