Twitter Message Recommendation Based on User Interest Profiles

RAHELEH MAKKI; AXEL J. SOTO; STEPHEN BROOKS; EVANGELOS MILIOS

San Francisco

Conferencia; 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM); 2016

IEEE/ACM

Twitter has become one of the most important platforms for gathering information, where users follow breaking news, track ongoing events and learn about their topics of interest. Considering the sheer volume of Twitter data and the ever-growing number of users, it is of great importance to have real-time systems that can monitor and recommend relevantand non-redundant tweets with respect to users' interests. In this paper, we propose a framework using language models as a basis for analyzing strategies and techniques for tweet recommendation based on user interest profiles. Results show that identifying named entities in profiles has a major impact on the accuracy of the recommender. We also performed a thorough comparison to investigate whether state-of-the-art semantic relatedness techniques have a positive impact on the precision of the recommended tweets. The TREC 2015 Microblog track dataset is used for comparison and evaluation throughout this paper.