Abstract
Lack of data and weak interpretability are the main problems faced by store site recommendations. This paper presents a unified site recommendation system called GeoGTI (General,Transferable and Interpretable), which applies to different brands in various industries. Different from the existing single-dimensional transfer methods, we adopt multi-layer knowledge transfer, leveraging knowledge from industries, competitive brands, upper administrative districts, and other cities to alleviate the problems of data scarcity and cold-start. Besides, to fill in the gap of weak interpretability, we score the candidate locations into a five-dimension radar chart from population, business, living, working, and transportation aspects, making the recommended result more convincing and instructive. Extensive experiments are conducted on real-world datasets from various industries, demonstrating GeoGTI’s practicability and effectiveness on store site recommendations.
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Notes
- 1.
Note that, considering user privacy, we only use aggregation or vague indicators.
- 2.
In order to prevent the problem of label leakage, when we initialize the model, we remove the target brand data and retrain the model.
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Supported by the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100) and the Fundamental Research Funds for the Central Universities.
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Gao, Y. et al. (2022). GeoGTI: Towards a General, Transferable and Interpretable Site Recommendation. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_49
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