LINK RECOMMENDER: Collaborative-Filtering for Recommending URLs to Twitter Users

Abstract

Twitter, the popular micro-blogging service, has gained a rapid growth in recent years. Newest information is accessible in this social web service through a large volume of real-time tweets. Tweets are short and they are more informative when they are coupled with URLs. Due to tweet overload in Twitter, we believe that an accurate URL recommender system is a beneficial tool for information seekers. In this paper, we focus on a neighborhood-based recommender system that recommends URLs to Twitter users. We consider one of the major elements of tweets, hashtags, as topic representatives of URLs in our approach. We propose methods for incorporating hashtags in measuring the relevancy of URLs. Our experiments show that our neighborhood-based recommender system outperforms the matrix factorization- based system significantly. We also show that the accuracy of URL recommendation in Twitter is time-dependent. A higher recommendation accuracy is obtained when more recent data is provided for recommendation.