A general graph-based model for recommendation in event-based social networks
A general graph-based model for recommendation in event-based social networks
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DOI：
10.1109/icde.2015.7113315
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
T. Pham;Xutao Li;G. Cong;Zhenjie Zhang
中科院分区：
文献类型：
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作者：
T. Pham;Xutao Li;G. Cong;Zhenjie Zhang
基于事件的社交网络（EBSN），如Meetings和Plancast，为用户提供计划，安排和发布事件的平台，已经越来越受欢迎和快速增长。EBSN不仅捕捉在线社交关系，还捕捉来自离线事件的离线交互。它们包含丰富的异构信息，包括用户、事件、组和标签等多种类型的实体及其交互关系。三个推荐任务，即向用户推荐组，向组推荐标签，并向用户推荐事件，已经在三个独立的研究中进行了探索。然而，所提出的方法都不能处理所有的三个推荐任务。在本文中，我们提出了一个通用的基于图的模型，称为HeteRS，在一个框架中解决EBSN上的三个推荐问题。我们的方法模型丰富的信息与异构图，并认为推荐问题作为一个查询相关的节点邻近度问题。为了解决具有挑战性的问题，加权不同类型的实体之间的影响，我们提出了一个学习计划来设置不同类型的实体之间的影响权重。在两个真实数据集上的实验结果表明，我们提出的方法在所有三个推荐任务上都明显优于最先进的方法，并且学习到的影响权重有助于理解用户行为。
Event-based social networks (EBSNs), such as Meetup and Plancast, which offer platforms for users to plan, arrange, and publish events, have gained increasing popularity and rapid growth. EBSNs capture not only the online social relationship, but also the offline interactions from offline events. They contain rich heterogeneous information, including multiple types of entities, such as users, events, groups and tags, and their interaction relations. Three recommendation tasks, namely recommending groups to users, recommending tags to groups, and recommending events to users, have been explored in three separate studies. However, none of the proposed methods can handle all the three recommendation tasks. In this paper, we propose a general graph-based model, called HeteRS, to solve the three recommendation problems on EBSNs in one framework. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities. Experimental results on two real-world datasets demonstrate that our proposed method significantly outperforms the state-of-the-art methods for all the three recommendation tasks, and the learned influence weights help understanding user behaviors.