Optimal Transport Enhanced Cross-City Site Recommendation
Authors: 
Xinhang Li, Xiangyu Zhao, Zihao Wang, Yang Duan, Yong Zhang, Chunxiao XingAuthors Info & Claims
Pages 1441 - 1451
Abstract
Site recommendation, which aims at predicting the optimal location for brands to open new branches, has demonstrated an important role in assisting decision-making in modern business. In contrast to traditional recommender systems that can benefit from extensive information, site recommendation starkly suffers from extremely limited information and thus leads to unsatisfactory performance. Therefore, existing site recommendation methods primarily focus on several specific name brands and heavily rely on fine-grained human-crafted features to avoid the data sparsity problem. However, such solutions are not able to fulfill the demand for rapid development in modern business. Therefore, we aim to alleviate the data sparsity problem by effectively utilizing data across multiple cities and thereby propose a novel Optimal Transport enhanced Cross-city (OTC) framework for site recommendation. Specifically, OTC leverages optimal transport (OT) on the learned embeddings of brands and regions separately to project the brands and regions from the source city to the target city. Then, the projected embeddings of brands and regions are utilized to obtain the inference recommendation in the target city. By integrating the original recommendation and the inference recommendations from multiple cities, OTC is able to achieve enhanced recommendation results. The experimental results on the real-world OpenSiteRec dataset, encompassing thousands of brands and regions across four metropolises, demonstrate the effectiveness of our proposed OTC in further improving the performance of site recommendation models.
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Index Terms
- Optimal Transport Enhanced Cross-City Site Recommendation
Recommendations
OpenSiteRec: An Open Dataset for Site Recommendation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalAs a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern ...
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Published In
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
- General Chairs:
- Grace Hui Yang,
- Hongning Wang,
- Sam Han,
- Program Chairs:
- Claudia Hauff,
- Guido Zuccon,
- Yi Zhang
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 11 July 2024
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- APRC - CityU New Research Initiatives
- Research Impact Fund
- CCF-Ant Research Fund
- Ant Group Research Fund
- CCF-Tencent Open Fund
- Tencent Rhino-Bird Focused Research Program
- Hong Kong ITC Innovation and Technology Fund Midstream Research Programme for Universities Project
- Hong Kong Environmental and Conservation Fund
- Kuaishou
- SIRG - CityU Strategic Interdisciplinary Research Grant
- Huawei Innovation Research Program
- CityU - HKIDS Early Career Research Grant
- CCF-BaiChuan-Ebtech Foundation Model Fund
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SIGIR 2024
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SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 14 - 18, 2024
Washington DC, USA
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