Recommending Related Microblogs: A Comparison Between Topic and WordNet based Approaches

Authors

  • Xing Chen Wuhan University of Technology
  • Lin Li Wuhan University of Technology
  • Guandong Xu Victoria University
  • Zhenglu Yang The University of Tokyo
  • Masaru Kitsuregawa The University of Tokyo

DOI:

https://doi.org/10.1609/aaai.v26i1.8431

Keywords:

WordNet, Microblogs, Similarity

Abstract

Computing similarity between short microblogs is an important step in microblog recommendation. In this paper, we investigate a topic based approach and a WordNet based approach to estimate similarity scores between microblogs and recommend top related ones to users. Empirical study is conducted to compare their recommendation effectiveness using two evaluation measures. The results show that the WordNet based approach has relatively higher precision than that of the topic based approach using 548 tweets as dataset. In addition, the Kendall tau distance between two lists recommended by WordNet and topic approaches is calculated. Its average of all the 548 pair lists tells us the two approaches have the relative high disaccord in the ranking of related tweets.

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Published

2021-09-20

How to Cite

Chen, X., Li, L., Xu, G., Yang, Z., & Kitsuregawa, M. (2021). Recommending Related Microblogs: A Comparison Between Topic and WordNet based Approaches. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 2417-2418. https://doi.org/10.1609/aaai.v26i1.8431

Issue

Vol. 26 No. 1 (2012): Twenty-Sixth AAAI Conference on Artificial Intelligence

Section

Student Abstract Track