User interests identification on Twitter using a hierarchical knowledge base
|User interests identification on Twitter using a hierarchical knowledge base|
|Author(s)||Kapanipathi P., Jain P., Venkataramani C., Sheth A.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Hierarchical Interest Graph, Personalization, Semantics, Social Web, Twitter, User Profiles, Wikipedia (Extra: Knowledge based systems, Knowledge management, Semantic Web, Semantics, Social networking (online), Hierarchical Interest Graph, Personalizations, Social webs, Twitter, User profile, Wikipedia, Hierarchical systems)|
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User interests identification on Twitter using a hierarchical knowledge base is a 2014 conference paper written in English by Kapanipathi P., Jain P., Venkataramani C., Sheth A. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Twitter, due to its massive growth as a social networking platform, has been in focus for the analysis of its user generated content for personalization and recommendation tasks. A common challenge across these tasks is identifying user interests from tweets. Semantic enrichment of Twitter posts, to determine user interests, has been an active area of research in the recent past. These approaches typically use available public knowledge-bases (such as Wikipedia) to spot entities and create entity-based user profiles. However, exploitation of such knowledge-bases to create richer user profiles is yet to be explored. In this work, we leverage hierarchical relationships present in knowledge-bases to infer user interests expressed as a Hierarchical Interest Graph. We argue that the hierarchical semantics of concepts can enhance existing systems to personalize or recommend items based on a varied level of conceptual abstractness. We demonstrate the effectiveness of our approach through a user study which shows an average of approximately eight of the top ten weighted hierarchical interests in the graph being relevant to a user's interests.
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