A Data-Driven Approach for Twitter Hashtag Recommendation

Abstract

This paper addresses the hashtag recommendation problem using high average-utility pattern mining. We introduce a novel framework called PM-HRec (Pattern Mining for Hashtag Recommendation). It consists of two main stages. First, offline processing transforms the corpus of tweets into a transactional database considering the temporal information of the tagged tweets (tweets with hashtags). The method discovers the temporal top k high average utility patterns. Irrelevant tagged tweets and the ontology of tagged tweets are also constructed offline. Second, an online processing inputs the utility patterns, the ontology, and the irrelevant tagged tweets to extract the most relevant hashtags for a given orpheline tweet (tweet without hashtags). Extensive experiments were carried out on large tweets collections. The proposed PM-HRec outperforms the existing state of the art hashtag recommendation approaches in terms of quality of recommended hashtags and runtime processing.

Keywords

• High average utility patterns
• hashtag recommendation
• ontology construction
• temporal information