Combining Algorithms for Recommendation System on Twitter

Abstract

Twitter has rapidly increased in popularity over the past few years. So, we have focused on Twitter as it has a large scale of data which is increasingly difficult to search through. In this paper, we propose recommendations for content on Twitter. We explored four dimensions in designing such as: topic relevance of content sources, the content candidate set for users, social voting and Meta data mapping. We implemented 24 algorithms for analysis of 12,000 records for three domains as follows: entertainment, stock exchange and smart phone in the design space. The best performing algorithm improved the percentage of correct matching interesting content to 23.86%.