Improved Hashtag Recommendation Algorithm Determining Appropriate Hashtags for Words with Different Meanings

• Published in: The review of socionetwork strategies Vol. 19; no. 1; pp. 1 - 17
• Main Authors: Kamino, Etsutaro, Kita, Eisuke
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.04.2025; Springer Nature B.V
• Subjects: Algorithms; Business and Management; Datasets; Information Systems Applications (incl.Internet); Input devices; IT in Business; Labels; Prediction models; Predictions; Questionnaires; Ratings & rankings; Simulation and Modeling; Tagging; Tags; User satisfaction; Variables; Words (language); Hashtag; Social network service (SNS); Different meaning; Co-occurrence; Recommendation; Common objects in context
• ISSN: 2523-3173; 1867-3236
• DOI: 10.1007/s12626-024-00173-3

In image-posting social networking services, such as Instagram, recommendation of appropriate hashtags for posts is vital. In the existing methods, a hashtag is searched using the names of object labels included in images added to posts as hashtags, and a relevance prediction model is applied to hashtags that appear most frequently among those attached to posts obtained from the search. Hashtags that are considered highly relevant to the post are then recommended to the user. However, it is difficult to recommend adequate hashtags relevant to a post containing a label that refers to different objects, such as “mouse,” which can refer to a “computer input device” and an “animal.” In this study, we developed algorithms (Algorithms 1 and 2) that employ additional labels related to object labels in posts to solve this problem. As additional labels, Algorithm 1 uses the other labels in the same object category in the Microsoft Common Objects in Context (COCO) image database, and Algorithm 2 uses words translated into six other languages. We also developed Algorithm 3, which is a hybrid of Algorithms 1 and 2. Based on user questionnaires, the hashtags suggested by Algorithms 1 and 2 are highly relevant to the posts: compared to an existing algorithm, the relevance of the hashtags improved by 18% and 64%, respectively.