Inferring semantic interest profiles from Twitter followees: does Twitter know better than your friends?
Inferring semantic interest profiles from Twitter followees: does Twitter know better than your friends?
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DOI：
10.1145/2851613.2851819
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
Christoph Besel;Jörg Schlötterer;M. Granitzer
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文献类型：
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作者：
Christoph Besel;Jörg Schlötterer;M. Granitzer
基于社交媒体的推荐系统从用户的社交网络活动推断用户的兴趣，以便提供个性化推荐。通常，通过分析用户的帖子或推文来生成用户简档。然而，用户生产的产品和消费的产品之间可能存在显著差异。我们提出了一种方法来推断用户的兴趣从followees（用户遵循的帐户），而不是推文。这是通过使用英语维基百科作为知识库从用户的关注者中提取命名实体并将其视为兴趣来完成的。之后，在维基百科类别分类上执行扩散激活算法，以将各种兴趣聚合到更抽象的兴趣简档。在我们的评估中，10个项目中有7个与用户相关，我们表明这种方法可以与最先进的技术竞争，并且在预测用户的兴趣方面比他们的人类朋友表现得更好。
Social media based recommendation systems infer users' interests from their social network activity in order to provide personalised recommendations. Typically, the user profiles are generated by analysing the users' posts or tweets. However, there might be a significant difference between what a user produces and what she consumes. We propose an approach for inferring user interests from followees (the accounts the user follows) rather than tweets. This is done by extracting named entities from a user's followees using the English Wikipedia as knowledge base and regarding them as interests. Afterwards, a spreading activation algorithm is performed on a Wikipedia category taxonomy to aggregate the various interests to a more abstract interest profile. With over 7 out of 10 items being relevant to the users in our evaluation, we show that this approach can compete with the state of the art and performs even better in predicting the users' interests than their human friends do.