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.
Highlights
A hashtag is a type of metadata tag which is widely used on the variants of social networks, e.g., twitter or facebook
Motivated by the success of pattern mining approach for solving the variants of realistic problems [9]–[11], this paper proposes a new framework called PM-HRec (Pattern Mining for Hashtag Recommendation), which exploits different correlations and dependencies between the tagged tweets to find out suitable hashtags for the orpheline tweets
CONTRIBUTION To answer to the previous issues, this paper proposes a new model for hashtag recommendation called temporal top k high average utility pattern mining and a framework
Summary
A hashtag is a type of metadata tag which is widely used on the variants of social networks, e.g., twitter or facebook. Motivated by the success of pattern mining approach for solving the variants of realistic problems [9]–[11], this paper proposes a new framework called PM-HRec (Pattern Mining for Hashtag Recommendation), which exploits different correlations and dependencies between the tagged tweets to find out suitable hashtags for the orpheline tweets. B. CONTRIBUTION To answer to the previous issues, this paper proposes a new model for hashtag recommendation called temporal top k high average utility pattern mining and a framework. A new hashtag recommendation framework called PMHRec is proposed by incorporating our temporal high average pattern mining model into the hashtag retrieval process This model is non sensitive to the number of tagged tweets | | thanks to the rules-base system of the temporal top k high average utility patterns extracted during the offline processing step.
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