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User interest prediction over future unobserved topics on social networks
Information Retrieval Journal ( IF 2.5 ) Pub Date : 2018-07-10 , DOI: 10.1007/s10791-018-9337-y
Fattane Zarrinkalam , Mohsen Kahani , Ebrahim Bagheri

The accurate prediction of users’ future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current interests and little work has been done on the prediction of user potential interests in the future. There have been work that attempt to identify a user future interests, however they cannot predict user interests with regard to new topics since these topics have never received any feedback from users in the past. In this paper, we propose a framework that works on the basis of temporal evolution of user interests and utilizes semantic information from knowledge bases such as Wikipedia to predict user future interests and overcome the cold item problem. Through extensive experiments on a real-world Twitter dataset, we demonstrate the effectiveness of our approach in predicting future interests of users compared to state-of-the-art baselines. Moreover, we further show that the impact of our work is especially meaningful when considered in case of cold items.

中文翻译:

用户对社交网络上未来未观察到的话题的兴趣预测

准确预测用户在社交网络上的未来兴趣,可以通过研究如果将来出现某些主题时用户的反应来执行将来的计划。它可以改善诸如定向广告和有效交付服务等领域。尽管预测社交网络上用户未来兴趣的重要性,但是现有工作主要集中在识别用户当前兴趣上,而在预测用户未来兴趣方面所做的工作很少。已经进行了尝试识别用户未来兴趣的工作,但是由于过去这些主题从未收到用户的任何反馈,因此它们无法预测用户对新主题的兴趣。在本文中,我们提出了一个框架,该框架可在用户兴趣的时间演变的基础上工作,并利用诸如Wikipedia之类的知识库中的语义信息来预测用户未来的兴趣并克服寒意问题。通过在真实世界的Twitter数据集上进行的广泛实验,我们证明了与最新基准相比,该方法在预测用户未来兴趣方面的有效性。此外,我们进一步表明,在考虑到寒冷物品的情况下,我们工作的影响尤其有意义。
更新日期:2018-07-10
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