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• W2977653183 abstract "In social networking/microblogging environments, #tag is often used for categorizing messages and marking their key points. Also, since some social networks such as twitter apply restrictions on the number of characters in messages, #tags can serve as a useful tool for helping users express their messages. In this paper, a new knowledge-intensive content-based #tag recommendation system is introduced. The proposed system works by integrating structured knowledge in every core component. First, the relevant features, semantic structures and information-content are extracted from messages. Since little information can often be placed in a message, a content enrichment module is introduced to identify information structures that can improve the representation of message. The extracted features are represented by semantic network. Then, a hybrid and multi-layered similarity module identifies the commonalities and differences of the features, semantics and information-content in messages. At the end, #tags are recommended to users based on #tags in contextually similar messages. The system is evaluated on Tweets2011 dataset. The results suggests that the proposed method can recommend suitable #tags in negligible operational time and when little content is available." @default.
• W2977653183 created "2019-10-10" @default.
• W2977653183 creator A5050131337 @default.
• W2977653183 creator A5052917472 @default.
• W2977653183 date "2019-08-01" @default.
• W2977653183 modified "2023-09-26" @default.
• W2977653183 title "Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach" @default.
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• W2977653183 doi "https://doi.org/10.5829/ije.2019.32.08b.06" @default.
• W2977653183 hasPublicationYear "2019" @default.
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