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Collaborative User Clustering for Short Text Streams

Liang, S; Ren, Z; Yilmaz, E; Kanoulas, E; (2017) Collaborative User Clustering for Short Text Streams. In: (Proceedings) Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). AAAI Press

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Abstract

In this paper, we study the problem of user clustering in the context of their published short text streams. Clustering users by short text streams is more challenging than in the case of long documents associated with them as it is difficult to track users' dynamic interests in streaming sparse data. To obtain better user clustering performance, we propose a user collaborative interest tracking model (UCIT) that aims at tracking changes of each user's dynamic topic distributions in collaboration with their followees', based both on the content of current short texts and the previously estimated distributions. We evaluate our proposed method via a benchmark dataset consisting of Twitter users and their tweets. Experimental results validate the effectiveness of our proposed UCIT model that integrates both users' and their collaborative interests for user clustering by short text streams.

Type: Proceedings paper
Title: Collaborative User Clustering for Short Text Streams
Event: Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
Location: San Francisco, California, USA
Dates: 04 February 2017 - 09 February 2017
Publisher version: https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/v...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10070799
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