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International Journal of Engineering، جلد ۳۲، شماره ۸، صفحات ۱۱۰۱-۱۱۱۶
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عنوان انگلیسی Automatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach
چکیده انگلیسی مقاله 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.
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نویسندگان مقاله Morteza Jaderyan |
Department of Computer Engineering, Bu Ali Sina University, Hamedan, Iran

Hassan Khotanlou |
Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran

نشانی اینترنتی http://www.ije.ir/article_89994_65851a3cb68cfc1e6e0519244a668820.pdf
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/409/article-409-2061538.pdf
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زبان مقاله منتشر شده en
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