Researchers interests finding has been an active area of investigation for different recommendation tasks. Previous approaches for finding researchers interests exploit writing styles and links connectivity by conside...
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Researchers interests finding has been an active area of investigation for different recommendation tasks. Previous approaches for finding researchers interests exploit writing styles and links connectivity by considering time of documents, while semantics-based intrinsic structure of words is ignored. Consequently, a topic model named Author-topic model is proposed, which exploits semantics-based intrinsic structure of words present between the authors of research papers. It ignores simultaneous modeling of time factor which results in exchangeability of topics problem, which is important factor to deal with when finding dynamic research interests. For example, in many real world applications, like finding reviewers for papers and finding taggers in the social tagging systems one need to consider different time periods. In this paper, we present time topic modeling approach named Temporal-Author-topic (TAT) which can simultaneously model text, researchers and time of research papers to overcome the exchangeability of topics problem. The mixture distribution over topics is influenced by both co-occurrences of words and timestamps of the research papers. Consequently, topics occurrence and their related researchers change over time, while the meaning of particular topic almost remains unchanged. Proposed approach is used to discover topically related researchers for different time periods. We also show how their interests and relationships change over a time period. Empirical results on large research papers corpus show the effectiveness of our proposed approach and dominance over Author-topic (AT) model, by handling the exchangeability of topics problem, which enables it to obtain similar meaning of particular topic overtime. (C) 2011 Elsevier B.V. All rights reserved.
Social media systems are very popular in today's dynamic web. One of the famous social media systems is Twitter, in which peoples used to share their personal ideas about current issues with their friends. This wo...
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ISBN:
(纸本)9781509064151;9781509064144
Social media systems are very popular in today's dynamic web. One of the famous social media systems is Twitter, in which peoples used to share their personal ideas about current issues with their friends. This work focuses on the problem of discovering a user's interest over time on twitter. Previous approaches have used to model the user topic of interest on twitter by building the profile of the users, that contain the words which can be used the user in his or her conversions with other users, but on twitter users used the noisy words which does not represent the correct topics or topic related to interest. This model has extended by a novel framework by using twitter user model. This model uses the latent topic variable to indicate the relatedness of the topic with any user. In this work, we propose a Temporal User topic(TUT) approach which can consider the text of tweet by any user and time of the tweet. The proposed approach is used to discover topically related Users for different time periods. We also show how the interests and relationships of these users are changeovers a time period.
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