Hashtag Recommendation Based on Multi-Features of Microblogs

Kou, Feifei; Du, Junping; Yang, Congxian; Shi, Yansong; Cui, Wanqiu; Liang, Meiyu; Geng, Yingsan

doi:10.1007/s11390-018-1851-2

Cited by 57 publications

(17 citation statements)

References 33 publications

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“…Given a user, hashtags extracted from like-minded users and similar tweets were retrieved, ranked, and recommended. Kou et al [66] recommended hashtags based on the weight of combining three features: content similarity, collaborative filtering of users with similar hashtag usage and topical interests.…”

Section: Behavioural Collaborative Filteringmentioning

confidence: 99%

Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

2021

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Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on hashtag recommendation for tweets and present insights from previous research papers. In this paper, we search for articles related to our research between 2010 and 2020 from CiteSeer, IEEE Xplore, Springer and ACM digital libraries. From the 61 articles included in this study, we notice that most of the research papers were focused on the textual content of tweets instead of other data. Furthermore, collaborative filtering methods are seldom used solely in hashtag recommendation. Taking this perspective, we present a taxonomy of hashtag recommendation based on the research methodologies that have been used. We provide a critical review of each of the classes in the taxonomy. We also discuss the challenges remaining in the field and outline future research directions in this area of study.

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Section: Behavioural Collaborative Filteringmentioning

confidence: 99%

Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

2021

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“…A collapsed Gibbs sampling model is used to infer hidden topics from the visual and textual generative model and then recommend new hashtags by using ranking score function. Kou et al [37] developed the hashtag recommendation based on multi-features of microblogs (HRMF). It considers hashtags of friendly users of different microblogs as the candidate hashtags.…”

Section: B Hashtag Recommendationmentioning

confidence: 99%

A Data-Driven Approach for Twitter Hashtag Recommendation

et al. 2020

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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. INDEX TERMS High average utility patterns, hashtag recommendation, ontology construction, temporal information.

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“…Over the past years, the principal research about the social network focuses on the effect of social data analysis [9][10][11][12] . With the joint efforts of scholars in the world, the semantics analysis method represented by Biterm topic model (BTM) [2] has been well studied and has been widely used in the information searching and recommendation field during the last years [13][14][15] . Nevertheless, traditional models cannot be used directly to detect topics in the data stream.…”

Section: Related Workmentioning

confidence: 99%