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%.
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