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Mining User Interest and Its Evolution for Recommendation on the Micro-blogging System

机译：挖掘用户兴趣及其对微博系统推荐的演变

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摘要

Different users have different needs, it is increasingly difficult to recommend interested topics to them. The micro-blogging system can expose user interests from individual behaviors along with his/her social connections. It also offers an opportunity to investigate how a large-scale social system recommends personal preferences according to the temporal, spatial and topical aspects of users activity. Here we focus on the problem of mining user interest and modeling its evolution on the micro-blogging system for recommendation. We learn the user preference on topics from the visited micro-bloggings as user interest using text mining techniques. We then extend this concept with user's social connection on different topics. Moreover, we study the evolution of the user interest model and finally recommend the most preferred micro-bloggings to a user. Experiments on a large scale of micro-blogging dataset shows that our model outperforms traditional approaches and achieves considerable performance on recommending interested posts to a user.
机译：不同的用户有不同的需求，向他们推荐感兴趣的主题变得越来越困难。微博系统可以从用户的个人行为以及他/她的社交关系中揭示用户的兴趣。它还提供了一个机会来调查大型社交系统如何根据用户活动的时间，空间和主题方面来推荐个人喜好。在这里，我们着重于挖掘用户兴趣并在微博系统上对其发展进行建模以进行推荐的问题。我们使用文本挖掘技术从访问的微博中根据用户的兴趣来学习用户对主题的偏爱。然后，我们通过用户在不同主题上的社交关系来扩展此概念。此外，我们研究了用户兴趣模型的演变，最后向用户推荐了最喜欢的微博。在大规模微博客数据集上进行的实验表明，我们的模型优于传统方法，并且在向用户推荐感兴趣的帖子时取得了可观的性能。

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