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Recommending Spatio-Temporal Sequence in indoor space based on social network analysis (case study: scientific conference)
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Mohammad Malek *   , Mahdi Habibian , Mohammad Karimi |
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Abstract: (268 Views) |
Every year, various events such as exhibitions and conferences are held all over the world. An event consists of various actors, the most important of which are the participants and the items in that event. Participants need to get the most out of these events at the right time. Therefore, providing a sequence of space-time recommendations for an event such as a scientific conference is a very significant issue. The scientific conference environment is a dynamic space where different items are held in different places and times. On the other hand, the location of the user in the conference space is constantly changing. Therefore, recommendations to users should be made according to the location of the user and the time of the request. To find a solution for this problem, focusing on the case study of scientific conferences, we used the ability of recommender systems and social network analysis. The characteristics of the users were extracted from their pages on the social-scientific network ResearchGate and the Google Scholar website.
The suggested recommendation method is a combination of social filtering, content-based filtering, and Spatio-temporal filtering methods. The extracted social information from the sources is analyzed by social filtering methods. As a result, similar participants to the user are recognized. The content information of these recognized participants is used to improve recommendations and prevent over-specialization. In addition, social filtering analyzes the relationship among the participations to solve the cold start problem and by recognizing expert participation, it uses their content information to recommend to new users. Content information of the user and similar participation to this user inter the content-based filtering for each user. In this filtering, the similarity value of items with the content information of the user is calculated for each time window. For this purpose, using the spatial graph model, the conference space becomes a computing space. Space-time refinement by using this computing space and taking into account the location and time of the user's request and the items, suggests the most optimal item to the user and follows it by providing the space-time suggestions to the user. The performance of the system in providing suggestions to users was evaluated by a questionnaire. According to this survey, the accuracy of the recommendations made using the proposed method was evaluated at 74%, and the prioritization ability at 93%. Also, in the survey conducted, the ability to recognize experts for a new user was investigated. As a result, 76% of new users confirmed the authenticity of both introduced experts.
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Article number: 4 |
| Keywords: Recommendation of Spatio-Temporal Sequence, Social Networks, Spatio-Temporal Filtering, Recommendation in Events |
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Full-Text [PDF 935 kb]
(127 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2024/04/4
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Malek M, Habibian M, Karimi M. Recommending Spatio-Temporal Sequence in indoor space based on social network analysis (case study: scientific conference). JGST 2025; 14 (3) : 4 URL: http://jgst.issgeac.ir/article-1-1181-en.html
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