User Interest Tracking Framework Based On Concept And Location

Abstract

The social networks are the widely used mediumto connect people from various geographical locations. Media data sharing is the main objective of the social network environment. The micro blogging platforms promote streaming and sharing of information by users. And also users share information on their recent  activities, perspectives and privileges by narrow passages. The topic extraction or identification methods are applied to identify the relevant topic represented in the user short text values. The user interests are represented by the topic models. Dynamic user interests are identified from the streaming short text. The Twitter social media accounts are analyzed to cluster users with their dynamic interest. The word pairs are extracted each short text. The word pair set is built for the user level. The topic model is discovered with the word pair set. To estimate the users' interest, two interdisciplinary equity tracking topic models are applied. The models of long-term dependence based UCIT (UCIT-L) and short-term dependence based UCIT (UCIT) are designed with specified time-limit based on analysis. The UCIT-L topic model tracks the preferences of users at different time intervals, based on the subject distributions of users. The Gibbs sampling algorithm collapsed, optimized for UCIT and UCIT-L inference. The user interest tracking operations are built for the Tweet short text data values. The social media analysis is built with Integrated User Interest Tracking (IUIT) framework to group the users. User profiles, spatial, temporal andtextual features are integrated in the clustering process. Cluster frequency prediction and realignment operations are combined in the user tracking model. The cluster frequency estimation is performed to predict the suitable cluster levelswith accuracy considerations. Dynamic user interest changes are tracked with spatial and temporal features