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Mining user interests over active topics on social networks

机译：挖掘用户对社交网络上活跃主题的兴趣

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摘要

Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users.
机译：近年来，通过用户在社交网络上的活动来推断用户的兴趣已成为新兴的研究话题。现有的大多数方法在很大程度上依赖于用户的显式贡献（帖子），而忽略了用户的隐性兴趣，即用户未明确提及但可能感兴趣的潜在用户兴趣。给定一组活动主题，社交网络在指定的时间间隔内，我们的目标是通过考虑用户的显式和隐式兴趣来针对这些主题建立用户的兴趣档案。这样做的原因是，不能从其对社交网络的显式贡献中直接确定占大多数社交网络用户的搭便车和冷启动用户的利益。具体来说，为了推断用户的隐性兴趣，我们提出了一种基于图的链接预测方案，该方案在包含三种类型信息的表示模型上运行：用户对主题的明确贡献，用户之间的关系以及主题之间的相关性。通过对表示模型的不同变体进行广泛的实验，并考虑了同构链接和异构链接预测，我们研究了主题相关性和用户的同构关系如何影响推断用户的隐性兴趣的质量。与真实的Twitter数据集上的最新基准进行比较，证明了我们的模型在推论用户困惑和转推预测应用程序方面的兴趣方面的有效性。此外，我们进一步表明，在搭便车和冷启动用户的情况下，我们的工作产生的影响尤其有意义。

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