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Pavan Kapanipathi's talk at IBM's Frontiers of Cloud Computing and Big Data Workshop 2014. http://researcher.ibm.com/researcher/view_group_subpage.php?id=5565
Due to the increased adoption of social web, users, specifically Twitter users are facing information overload. Unless a user is willing to restrict the sources (eg number of followings), important information relevant to users' interests often go unnoticed. The reasons include (1) the postings may be at a time the user is not looking for; (2) the user unaware and hence not following the information source; (3) and the information arrives at a rate at which the user cannot consume. Furthermore, some information that are temporally relevant, discovered late might be of no use.
My research addresses these challenges by
(1) Generating user profiles of interests from Twitter using Wikipedia. The interests gleaned from users' Twitter data can be leveraged by personalization and recommendation systems in order to reduce information overload/Volume for users.
(2) Filtering twitter data relevant to dynamically evolving entities. Including Volume, this addresses the velocity challenge in delivering relevant information in real-time. The approach is deployed on Twitris to crawl for dynamic event-relevant tweets for analysis. The prominent aspect of the approaches is the use of crowd-sourced knowledge-base such as Wikipedia.