Abstract
Store site recommendation aims to predict the value of the store at candidate locations and then recommend the optimal location to the company for placing a new brick-and-mortar store. Most existing studies focus on learning machine learning or deep learning models based on large-scale training data of existing chain stores in the same city. However, the expansion of chain enterprises in new cities suffers from data scarcity issues, and these models do not work in the new city where no chain store has been placed (i.e., cold-start problem). In this article, we propose a unified approach for cold-start store site recommendation, Weighted Adversarial Network with Transferability weighting scheme (WANT), to transfer knowledge learned from a data-rich source city to a target city with no labeled data. In particular, to promote positive transfer, we develop a discriminator to diminish distribution discrepancy between source city and target city with different data distributions, which plays the minimax game with the feature extractor to learn transferable representations across cities by adversarial learning. In addition, to further reduce the risk of negative transfer, we design a transferability weighting scheme to quantify the transferability of examples in source city and reweight the contribution of relevant source examples to transfer useful knowledge. We validate WANT using a real-world dataset, and experimental results demonstrate the effectiveness of our proposed model over several state-of-the-art baseline models.
- Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009. Curriculum learning. In Proceedings of the 26th Annual International Conference on Machine Learning. 41–48.Google ScholarDigital Library
- Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Michael I. Jordan. 2018. Partial transfer learning with selective adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2724–2732.Google Scholar
- Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, and Qiang Yang. 2019. Learning to transfer examples for partial domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2985–2994.Google ScholarCross Ref
- Ziliang Chen, Jingyu Zhuang, Xiaodan Liang, and Liang Lin. 2019. Blending-target domain adaptation by adversarial meta-adaptation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2248–2257.Google ScholarCross Ref
- Thomas M. Cover and Joy A. Thomas. 2012. Elements of Information Theory. John Wiley & Sons.Google Scholar
- Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014. A deep convolutional activation feature for generic visual recognition. In International Conference on Machine Learning. 647--655.Google Scholar
- Lisheng Fu, Thien Huu Nguyen, Bonan Min, and Ralph Grishman. 2017. Domain adaptation for relation extraction with domain adversarial neural network. In Proceedings of the 8th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 425–429.Google Scholar
- Yaroslav Ganin and Victor Lempitsky. 2014. Unsupervised domain adaptation by backpropagation. In International Conference on Machine Learning. 1180--1189.Google Scholar
- Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The Journal of Machine Learning Research 17, 1 (2016), 2096–2030.Google ScholarDigital Library
- Behnam Gholami, Pritish Sahu, Ognjen Rudovic, Konstantinos Bousmalis, and Vladimir Pavlovic. 2020. Unsupervised multi-target domain adaptation: An information theoretic approach. IEEE Transactions on Image Processing 29 (2020), 3993–4002.Google ScholarCross Ref
- Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28th International Conference on Machine Learning. 513–520.Google Scholar
- Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A kernel two-sample test. Journal of Machine Learning Research 13, 1 (2012), 723–773.Google ScholarDigital Library
- Bin Guo, Jing Li, Vincent W. Zheng, Zhu Wang, and Zhiwen Yu. 2018. Citytransfer: Transferring inter-and intra-city knowledge for chain store site recommendation based on multi-source urban data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (2018), 1–23.Google ScholarDigital Library
- Pablo Jensen. 2006. Network-based predictions of retail store commercial categories and optimal locations. Physical Review E 74, 3 (2006), 035101.Google ScholarCross Ref
- Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander G. Hauptmann. 2015. Self-paced curriculum learning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence.Google Scholar
- Dmytro Karamshuk, Anastasios Noulas, Salvatore Scellato, Vincenzo Nicosia, and Cecilia Mascolo. 2013. Geo-spotting: Mining online location-based services for optimal retail store placement. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 793–801.Google ScholarDigital Library
- Jing Li, Bin Guo, Zhu Wang, Mingyang Li, and Zhiwen Yu. 2016. Where to place the next outlet? Harnessing cross-space urban data for multi-scale chain store recommendation. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. 149–152.Google ScholarDigital Library
- Yan Liu, Bin Guo, Nuo Li, Jing Zhang, Jingmin Chen, Daqing Zhang, Yinxiao Liu, Zhiwen Yu, Sizhe Zhang, and Lina Yao. 2019. DeepStore: An interaction-aware wide&deep model for store site recommendation with attentional spatial embeddings. IEEE Internet of Things Journal 6, 4 (2019), 7319–7333.Google ScholarCross Ref
- Zhaoyang Liu, Yanyan Shen, and Yanmin Zhu. 2018. Where will dockless shared bikes be stacked? —Parking hotspots detection in a new city. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 566–575.Google Scholar
- Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan. 2015. Learning transferable features with deep adaptation networks. In International Conference on Machine Learning. 97--105.Google Scholar
- Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan. 2017. Deep transfer learning with joint adaptation networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR.org, 2208–2217.Google Scholar
- Daniel Lowd and Christopher Meek. 2005. Adversarial learning. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. 641–647.Google ScholarDigital Library
- Liqiang Nie, Yongqi Li, Fuli Feng, Xuemeng Song, Meng Wang, and Yinglong Wang. 2020. Large-scale question tagging via joint question-topic embedding learning. ACM Transactions on Information Systems 38, 2 (2020), 1–23.Google ScholarDigital Library
- Liqiang Nie, Xiang Wang, Jianglong Zhang, Xiangnan He, Hanwang Zhang, Richang Hong, and Qi Tian. 2017. Enhancing micro-video understanding by harnessing external sounds. In Proceedings of the 25th ACM International Conference on Multimedia. 1192–1200.Google ScholarDigital Library
- Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang. 2010. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks 22, 2 (2010), 199–210.Google ScholarDigital Library
- Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2009), 1345–1359.Google ScholarDigital Library
- Xingchao Peng, Zijun Huang, Ximeng Sun, and Kate Saenko. 2019. Domain agnostic learning with disentangled representations. In International Conference on Machine Learning. 5102--5112.Google Scholar
- Guo-Jun Qi, Charu Aggarwal, Yong Rui, Qi Tian, Shiyu Chang, and Thomas Huang. 2011. Towards cross-category knowledge propagation for learning visual concepts. In Proceedings of the 2011 Conference on Computer Vision and Pattern Recognition. IEEE, 897–904.Google ScholarDigital Library
- Guo-Jun Qi, Charu C. Aggarwal, and Thomas Huang. 2013. Link prediction across networks by biased cross-network sampling. In Proceedings of the 2013 IEEE 29th International Conference on Data Engineering (ICDE’13). IEEE, 793–804.Google Scholar
- Guo-Jun Qi, Wei Liu, Charu Aggarwal, and Thomas Huang. 2016. Joint intermodal and intramodal label transfers for extremely rare or unseen classes. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 7 (2016), 1360–1373.Google ScholarCross Ref
- Guo-Jun Qi, Liheng Zhang, Hao Hu, Marzieh Edraki, Jingdong Wang, and Xian-Sheng Hua. 2018. Global versus localized generative adversarial nets. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1517–1525.Google ScholarCross Ref
- Darsh J. Shah, Tao Lei, Alessandro Moschitti, Salvatore Romeo, and Preslav Nakov. 2018. Adversarial domain adaptation for duplicate question detection. arXiv preprint arXiv:1809.02255 (2018).Google Scholar
- Yang Shu, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. 2019. Transferable curriculum for weakly-supervised domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 4951–4958.Google ScholarCross Ref
- Lingxiao Song, Man Zhang, Xiang Wu, and Ran He. 2018. Adversarial discriminative heterogeneous face recognition. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Google Scholar
- Lisa Torrey and Jude Shavlik. 2010. Transfer learning. In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques. IGI Global, 242–264.Google Scholar
- Eugene Tuv, Alexander Borisov, George Runger, and Kari Torkkola. 2009. Feature selection with ensembles, artificial variables, and redundancy elimination. Journal of Machine Learning Research 10 (2009), 1341–1366.Google ScholarDigital Library
- Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. 2017. Adversarial discriminative domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7167–7176.Google ScholarCross Ref
- Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. 2014. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014).Google Scholar
- Feng Wang, Li Chen, and Weike Pan. 2016. Where to place your next restaurant? Optimal restaurant placement via leveraging user-generated reviews. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 2371–2376.Google Scholar
- Jingdong Wang, Zhe Zhao, Jiazhen Zhou, Hao Wang, Bin Cui, and Guojun Qi. 2012. Recommending Flickr groups with social topic model. Information Retrieval 15, 3–4 (2012), 278–295.Google ScholarDigital Library
- Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang. 2018. Cross-city transfer learning for deep spatio-temporal prediction. arXiv preprint arXiv:1802.00386 (2018).Google Scholar
- Ying Wei, Yu Zheng, and Qiang Yang. 2016. Transfer knowledge between cities. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1905–1914.Google ScholarDigital Library
- Yanan Xu, Yanyan Shen, Yanmin Zhu, and Jiadi Yu. 2020. AR2Net: An attentive neural approach for business location selection with satellite data and urban data. ACM Transactions on Knowledge Discovery from Data 14, 2 (2020), 1–28.Google Scholar
- Jian Zeng and Bo Tang. 2019. Mining heterogeneous urban data for retail store placement. In Proceedings of the ACM Turing Celebration Conference. 1–5.Google ScholarDigital Library
- Jing Zhang, Zewei Ding, Wanqing Li, and Philip Ogunbona. 2018. Importance weighted adversarial nets for partial domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8156–8164.Google ScholarCross Ref
- Yiru Zhao, Zhongming Jin, Guo-jun Qi, Hongtao Lu, and Xian-sheng Hua. 2018. An adversarial approach to hard triplet generation. In Proceedings of the European Conference on Computer Vision (ECCV’18). 501–517.Google ScholarCross Ref
Index Terms
- Knowledge Transfer with Weighted Adversarial Network for Cold-Start Store Site Recommendation
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