Who, Where, When, and What
Who, Where, When, and What
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DOI：
10.1145/2699667
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
Quan Yuan;G. Cong;Kaiqi Zhao-;Zongyang Ma;Aixin Sun
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文献类型：
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作者：
Quan Yuan;G. Cong;Kaiqi Zhao-;Zongyang Ma;Aixin Sun
微博服务和基于位置的社交网络，如Twitter、微博和Foursquare，使用户能够发布带有时间戳和地理注释的短消息。这些地理信息短消息中包含的丰富的时空语义信息为普适计算环境中上下文感知应用的开发提供了良好的机遇。示例应用包括上下文推荐和上下文搜索。为了获得准确的推荐和最相关的搜索结果，重要的是捕获用户的上下文信息（例如，时间和位置）以及理解用户的主题兴趣和意图。虽然时间和位置可以很容易地被智能手机捕获，但理解用户的兴趣和意图需要有效的方法来建模用户移动行为。这里，用户移动性指的是谁在什么时间访问哪个地方进行什么活动。也就是说，用户移动行为建模必须考虑用户（谁），空间（哪里），时间（何时）和活动（什么）方面。不幸的是，没有以前的研究对用户移动行为建模考虑所有的四个方面联合起来，其中有复杂的相互依赖性。在我们的初步研究中，我们提出了第一个解决方案命名为W4（简称为谁，在哪里，何时，和什么），发现用户的移动行为从四个方面。在本文中，我们进一步增强了W4，并提出了一个名为EW4（Enhanced W4的缩写）的非参数贝叶斯模型。EW4不需要参数调整，在我们的实验中取得了比W4更好的结果。给定用户的四个方面中的一些（例如，时间），我们的模型能够推断其他方面的信息（例如，位置和主题词）。因此，我们的模型有各种上下文感知的应用程序，特别是在上下文搜索和推荐。在两个真实数据集上的实验结果表明，该模型能够有效地发现用户的时空主题。该模型还显著优于各种任务的最新基线，包括推文的位置预测和需求感知位置推荐。
Micro-blogging services and location-based social networks, such as Twitter, Weibo, and Foursquare, enable users to post short messages with timestamps and geographical annotations. The rich spatial-temporal-semantic information of individuals embedded in these geo-annotated short messages provides exciting opportunity to develop many context-aware applications in ubiquitous computing environments. Example applications include contextual recommendation and contextual search. To obtain accurate recommendations and most relevant search results, it is important to capture users’ contextual information (e.g., time and location) and to understand users’ topical interests and intentions. While time and location can be readily captured by smartphones, understanding user’s interests and intentions calls for effective methods in modeling user mobility behavior. Here, user mobility refers to who visits which place at what time for what activity. That is, user mobility behavior modeling must consider user (Who), spatial (Where), temporal (When), and activity (What) aspects. Unfortunately, no previous studies on user mobility behavior modeling have considered all of the four aspects jointly, which have complex interdependencies. In our preliminary study, we propose the first solution named W4 (short for Who, Where, When, and What) to discover user mobility behavior from the four aspects. In this article, we further enhance W4 and propose a nonparametric Bayesian model named EW4 (short for Enhanced W4). EW4 requires no parameter tuning and achieves better results over W4 in our experiments. Given some of the four aspects of a user (e.g., time), our model is able to infer information of the other aspects (e.g., location and topical words). Thus, our model has a variety of context-aware applications, particularly in contextual search and recommendation. Experimental results on two real-world datasets show that the proposed model is effective in discovering users’ spatial-temporal topics. The model also significantly outperforms state-of-the-art baselines for various tasks including location prediction for tweets and requirement-aware location recommendation.