Weakly Supervised Attention for Hashtag Recommendation using Graph Data

Javari, Amin; He, Zhankui; Huang, Zijie; Jeetu, Raj; Chang, Kevin Chen-Chuan

doi:10.1145/3366423.3380182

Cited by 17 publications

(12 citation statements)

References 25 publications

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“…Javari et al [22] looked into the hashtag recommendation from a different angle. They built PHAN, a graph-based model of representative users and hashtags.…”

Section: Social Collaborative Filteringmentioning

confidence: 99%

“…Although several studies [18,22,67] have highlighted the importance of user relations (behavioural and social), the degree of their influence is not clear. Alsini et al [16,19,23] present an overview of the community detection algorithms as techniques used to group like-minded users.…”

Section: Social Collaborative Filteringmentioning

confidence: 99%

“…Social recommendation systems can be defined as techniques or algorithms that automatically suggest the most relevant and interesting data to social media users. Hashtag recommendation is a branch of the social recommendation systems that proposes contemporary and relevant hashtags to users as they type tweets [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Choosing the correct hashtag has several benefits: it enables the user to quickly join a discussion and read tweets written by other users [24].…”

Section: Introductionmentioning

confidence: 99%

“…Although researchers have investigated various techniques and factors that affect the performance of hashtag recommendation methods for Twitter/Sina Weibo data [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], there is a lack of a systematic review of these methods. Jain et al [30] listed only 11 methods in their survey on hashtag recommendations.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Javari et al [22] looked into the hashtag recommendation from a different angle. They built PHAN, a graph-based model of representative users and hashtags.…”

Section: Social Collaborative Filteringmentioning

confidence: 99%

Section: Social Collaborative Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

2021

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“…The paper advances the state of the arts of information diffusion prediction, a topic that receives much recent attention in several contexts, including social recommendations [6,20,29], misinformation detection [35], polarization analysis [47], user personalization [4,39,41], among others. On social media, information is propagated rapidly through users' posting and forwarding behaviors, leading to information cascades consisting of the users who have forwarded the content (we call them original forwarding users) as well as the actual information content.…”

Section: Introductionmentioning

confidence: 99%

DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction

Wang

Huang

Liu

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for other potential users and predict the corresponding user rankings. Inferring user interests from diffusion data lies the foundation of diffusion prediction, because users often forward the information in which they are interested or the information from those who share similar interests. Their interests also evolve over time as the result of the dynamic social influence from neighbors and the time-sensitive information gained inside/outside the social media. Existing works fail to model users' intrinsic interests from the diffusion data and assume user interests remain static along the time. DyDiff-VAE advances the state of the art in two directions: (i) We propose a dynamic encoder to infer the evolution of user interests from observed diffusion data.(ii) We propose a dual attentive decoder to estimate the propagation likelihood by integrating information from both the initial cascade content and the forwarding user sequence. Extensive experiments on four real-world datasets from Twitter and Youtube demonstrate the advantages of the proposed model; we show that it achieves 43.3% relative gains over the best baseline on average. Moreover, it has the lowest run-time compared with recurrent neural network based models. CCS CONCEPTS• Information systems → Social networks; • Computing methodologies → Neural networks.

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Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

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Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.

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Weakly Supervised Attention for Hashtag Recommendation using Graph Data

Cited by 17 publications

References 25 publications

Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

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