Abstract: With the rise of homestays and online short-term rental platforms, the phenomenon of host multiple ownership continues to receive attention and research. This phenomenon provides a new research perspective, and how to identify same-source hosts on different platforms has become the first problem to be solved. Therefore, this article explores the C2C online short-term rental cross-platform host matching algorithm based on traditional user matching. Among them, due to the sparse personal information of the host, this paper introduces housing information and designs a two-stage host matching algorithm (TSHM) based on housing. The method in this paper achieves 99.69% and 81.97% accuracy on the common data set and the hard-case data set based on the real data of the two domestic online short-term rental platforms, respectively, which is better than traditional classifiers such as SVM and DT. The matching model is verified. The effectiveness of the matching features provides a new idea for cross-platform host matching, which can still effectively match the host even if the host’s personal information is lacking. However, this article only conducts experiments on domestic platform data, and does not introduce features such as text and pictures, which has certain limitations.

Key words: online short-term rent, host matching, genetic algorithm, multi-homing