Bilateral Matching Decision for UGC Platform: A Simulation Analysis Using the Simulated Annealing Algorithm
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In the unique context of sudden global public health events, the fragmented time of a vast number of users has transformed into a substantial influx of attention resources onto user-generated content (UGC) platforms. The escalating contrast between the swift expansion of decentralized content and the immense user attention resources has brought forth heightened demands for effective platform matching. This study addresses the bilateral matching challenge on UGC platforms by introducing the concepts of attention resource pool and content pool. A one-to-many bilateral matching framework is proposed under the attention resources theory, and a multi-objective matching model is constructed based on the dual satisfaction of UGC content creators and consumers, with a central focus on platform evaluation. To optimize the multi-objective function, a simulated annealing algorithm is devised, and MATLAB software is employed to simulate real data from a short video platform. The findings demonstrate that platform evaluation plays a pivotal role in UGC platform bilateral matching, and efficient UGC bilateral matching can be achieved by leveraging the intermediary role of the platform. Overall satisfaction, as perceived by bilateral users, surpasses that from the standpoint of individual users. Additionally, this study reveals that variations in the upper limit of content generation users' matching have negligible impact on overall satisfaction, suggesting that UGC platform enterprises' emphasis on content generation exposure levels does not enhance bilateral matching efficiency but may lead to excessive traffic skew and harm the industry ecology.
UGC platform; two-sided matching; attention resources; evaluation from platform; simulated annealing algorithm