Collaborative User Clustering for Short Text Streams

Authors

  • Shangsong Liang University College London
  • Zhaochun Ren University College London
  • Emine Yilmaz University College London
  • Evangelos Kanoulas University of Amsterdam

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.

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Published

2017-02-12

How to Cite

Liang, S., Ren, Z., Yilmaz, E., & Kanoulas, E. (2017). Collaborative User Clustering for Short Text Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11011