Semantic Matching Efficiency of Supply and Demand Texts on Online Technology Trading Platforms: Taking the Electronic Information of Three Platforms as an Example |
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| Institution: | 1. School of economics and management, Beijing University of Technology, No. 100 Ping Le Yuan, Chaoyang District, Beijing, 100124, China;2. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;1. Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece;2. School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;1. IT Research Institute, Chosun University, Gwangju, South Korea;2. Data Storage Technology INT, Seoul, South Korea;3. Konkuk University, Seoul, South Korea;4. Jan Evangelista Purkyne University, Usti nad Labem, Czech Republic;5. ISEP, IPP, Porto, Portugal;6. Chosun University, Gwangju, South Korea;1. Universidad Autónoma de Madrid, Escuela Politécnica Superior, C/Francisco Tomás y Valiente,11,Madrid, 28049, Spain;2. School of Computing Science, University of Glasgow, Lilybank Gardens, G12 8QQ, Glasgow, Scotland, United Kingdom;1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;2. Baidu Inc., Beijing, China;3. Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Science, Tianjin Normal University, Tianjin 300387, China;1. Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain;2. Facultad de Ciencias Políticas y Sociología, Universidad Complutense de Madrid, Spain;3. Knowledge Media Institute, The Open University, United Kingdom |
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| Abstract: | We calculated the matching values of technology supply and demand texts based on texts semantic similarity with Word2Vec and Cosine similarity algorithms, and then proposed a new index named Supply-Demand Matching Efficiency (SDME) to measure the matching efficiency of online technology trading platforms (OTTPs). Through the empirical research on the three types of OTTPs, the findings are as follows: First, the SDME of Zhejiang Market (Government-Owned, Government-Operated, GOGO), Technology E Market (Government-Owned, Contractor-Operated, GOCO), and Keyi Market (Market-Owned, Market-Operated, MOMO) are 64.69%, 54.38% and 28.99% respectively, indicating that the government plays an important role in attracting effective technology suppliers and demanders to participate in online trade and standardizing information expression, thereby improving the SDME. Second, by comparing the SDME and the newly announced signing rate of each OTTP, we found that the OTTP with high SDME also has high signing rate, and the changing trend of the two is consistent. Third, we used the TextRank and Latent Dirichlet Allocation (LDA) to study the topic distribution of technology supply and demand, and calculated the topic differences of each OTTP, which are 70%, 75%, 84% respectively. The Technology E Market and Zhejiang Market have low topic differences and high SDME, while Keyi Market has high topic differences and low SDME, which indicated that the topic differences have a negative effect on SDME. Intuitively, measuring the semantic matching efficiency of supply and demand texts on OTTPs can help the suppliers and demanders to retrieve information accurately, and assist the OTTPs to carry out trade promotion and evaluate trade performance. |
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