ABSTRACT
Identifying and recommending potential new customers for local businesses are crucial to the survival and success of local businesses. A key component to identifying the right customers is to understand the decision-making process of choosing a business over the others. However, modeling this process is an extremely challenging task as a decision is influenced by multiple factors. These factors include but are not limited to an individual's taste or preference, the location accessibility of a business, and the reputation of a business from social media. Most of the recommender systems lack the power to integrate multiple factors together and are hardly extensible to accommodate new incoming factors. In this paper, we introduce a unified framework, CORALS, which considers the personal preferences of different customers, the geographical influence, and the reputation of local businesses in the customer recommendation task. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 state-of-the-art methods using two real-world datasets. The results demonstrate that CORALS outperforms all these baselines by a significant margin in most scenarios. In addition to identifying potential new customers, we also break down the analysis for different types of businesses to evaluate the impact of various factors that may affect customers' decisions. This information, in return, provides a great resource for local businesses to adjust their advertising strategies and business services to attract more prospective customers.
- Judd Antin, Marco de Sá, and Elizabeth F. Churchill. 2012. Local experts and online review sites. In CSCW '12, Seattle, WA, USA, February 11--15, 2012 - Companion Volume . Google ScholarDigital Library
- Jie Bao, Yu Zheng, and Mohamed F. Mokbel. 2012. Location-based and preference-aware recommendation using sparse geo-social networking data. In SIGSPATIAL '12, Redondo Beach, CA, USA, November 7--9, 2012 . Google ScholarDigital Library
- BrightLocal. 2016. Local Consumer Review Survey . https://www.brightlocal.com/learn/local-consumer-review-survey/.Google Scholar
- Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks. In AAAI '12, July 22--26, 2012, Toronto, Ontario, Canada. Google ScholarDigital Library
- Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In SIGKDD '11, San Diego, CA, USA, August 21--24, 2011 . Google ScholarDigital Library
- Josh Constine. 2017. Facebook relaunches Events app as Facebook Local, adds bars and food . https://techcrunch.com/2017/11/10/facebook-local/.Google Scholar
- Arthur P Dempster, Nan M Laird, and Donald B Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological) (1977).Google Scholar
- John C. Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research, Vol. 12 (2011), 2121--2159. http://dl.acm.org/citation.cfm?id=2021068 Google ScholarDigital Library
- Brendan J Frey and Delbert Dueck. 2007. Clustering by passing messages between data points. science, Vol. 315, 5814 (2007).Google Scholar
- Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring temporal effects for location recommendation on location-based social networks. In RecSys '13, Hong Kong, China, October 12--16, 2013 . Google ScholarDigital Library
- Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2015. Content-Aware Point of Interest Recommendation on Location-Based Social Networks. In AAAI '15, January 25--30, 2015, Austin, Texas, USA. 1721--1727. Google ScholarDigital Library
- Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky. 2012. Overview of mini-batch gradient descent . http://www.cs.toronto.edu/ tijmen/csc321/slides/lecture_slides_lec6.pdf .Google Scholar
- Bo Hu and Martin Ester. 2013. Spatial topic modeling in online social media for location recommendation. In RecSys '13, Hong Kong, China, October 12--16, 2013 . Google ScholarDigital Library
- Bo Hu and Martin Ester. 2014. Social Topic Modeling for Point-of-Interest Recommendation in Location-Based Social Networks. In ICDM '14, Shenzhen, China, December 14--17, 2014. 845--850. Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In (ICDM '08), December 15--19, 2008, Pisa, Italy . Google ScholarDigital Library
- Quoc V. Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents. In ICML '14, Beijing, China, 21--26 June 2014 . Google ScholarDigital Library
- Huayu Li, Yong Ge, Richang Hong, and Hengshu Zhu. 2016. Point-of-Interest Recommendations: Learning Potential Check-ins from Friends. In SIGKDD '16, San Francisco, CA, USA, August 13--17, 2016 . Google ScholarDigital Library
- Xutao Li, Gao Cong, Xiaoli Li, Tuan-Anh Nguyen Pham, and Shonali Krishnaswamy. 2015. Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation. In SIGIR '15, Santiago, Chile, August 9--13, 2015 . 433--442. Google ScholarDigital Library
- Defu Lian, Yong Ge, Fuzheng Zhang, Nicholas Jing Yuan, Xing Xie, Tao Zhou, and Yong Rui. 2015. Content-Aware Collaborative Filtering for Location Recommendation Based on Human Mobility Data. In ICDM '15, Atlantic City, NJ, USA, November 14--17, 2015. 261--270. Google ScholarDigital Library
- Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In KDD '14, New York, USA - August 24 - 27, 2014 . Google ScholarDigital Library
- Moshe Lichman and Padhraic Smyth. 2014. Modeling human location data with mixtures of kernel densities. In KDD '14, New York, USA - August 24 - 27, 2014 . Google ScholarDigital Library
- Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In KDD '13, Chicago, IL, USA, August 11--14, 2013 . Google ScholarDigital Library
- Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, and Hui Xiong. 2016. Unified Point-of-Interest Recommendation with Temporal Interval Assessment. In SIGKDD '16, San Francisco, CA, USA, August 13--17, 2016 . Google ScholarDigital Library
- Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014. Exploiting Geographical Neighborhood Characteristics for Location Recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3--7, 2014. 739--748. Google ScholarDigital Library
- Maddy Osman. 2018. 28 Powerful Facebook Stats Your Brand Can't Ignore in 2018 . https://sproutsocial.com/insights/facebook-stats-for-marketers/.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI '09, Montreal, QC, Canada, June 18--21, 2009 . Google ScholarDigital Library
- Douglas A. Reynolds. 2009. Gaussian Mixture Models. In Encyclopedia of Biometrics .Google Scholar
- Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, and Alan Hanjalic. 2012. CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In RecSys '12, Dublin, Ireland, September 9--13, 2012 . Google ScholarDigital Library
- Craig Smith. 2016. By the numbers: 20 important Foursquare Stats . http://expandedramblings.com/index.php/by-the-numbers-interesting-foursquare-user-stats/.Google Scholar
- Waldo R Tobler. 1970. A computer movie simulating urban growth in the Detroit region. Economic geography, Vol. 46, sup1 (1970).Google Scholar
- Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, and Alexander J. Smola. 2007. COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking. In Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3--6, 2007 . Google ScholarDigital Library
- Markus Weimer, Alexandros Karatzoglou, and Alexander J. Smola. 2015. Improving maximum margin matrix factorization. Machine Learning, Vol. 72, 3 (2015). Google ScholarDigital Library
- Jason Weston, Samy Bengio, and Nicolas Usunier. 2011. WSABIE: Scaling Up to Large Vocabulary Image Annotation. In IJCAI '11, Barcelona, Catalonia, Spain, July 16--22, 2011 . Google ScholarDigital Library
- Jason Weston, Hector Yee, and Ron J. Weiss. 2013. Learning to rank recommendations with the k-order statistic loss. In RecSys '13, Hong Kong, China, October 12--16, 2013 . Google ScholarDigital Library
- Xian Wu, Yuxiao Dong, Baoxu Shi, Ananthram Swami, and Nitesh V. Chawla. 2018a. Who will Attend This Event Together? Event Attendance Prediction via Deep LS™ Networks. In Proceedings of the 2018 SIAM International Conference on Data Mining, SDM 2018, May 3--5, 2018, San Diego, CA, USA. 180--188.Google ScholarCross Ref
- Xian Wu, Yuxiao Dong, Jun Tao, Chao Huang, and Nitesh V. Chawla. 2017. Reliable fake review detection via modeling temporal and behavioral patterns. In 2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11--14, 2017. 494--499.Google Scholar
- Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, and Nitesh V. Chawla. 2018b. RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22--26, 2018. 1073--1082. Google ScholarDigital Library
- Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang. 2016. Learning Graph-based POI Embedding for Location-based Recommendation. In CIKM '16, Indianapolis, IN, USA, October 24--28, 2016 . Google ScholarDigital Library
- Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, and Jiawei Han. 2017. Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017. 1245--1254. Google ScholarDigital Library
- Zijun Yao, Yanjie Fu, Bin Liu, Yanchi Liu, and Hui Xiong. 2016. POI Recommendation: A Temporal Matching between POI Popularity and User Regularity. In ICDM '16, December 12--15, 2016, Barcelona, Spain .Google ScholarCross Ref
- Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR '11, Beijing, China, July 25--29, 2011 . Google ScholarDigital Library
- Yelp. 2017. Yelp for Business Owners . https://biz.yelp.com/support/what_is_yelp .Google Scholar
- Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, and Ling Chen. 2014. LCARS: A Spatial Item Recommender System. ACM Trans. Inf. Syst., Vol. 32, 3 (2014). Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat-Thalmann. 2013. Time-aware point-of-interest recommendation. In SIGIR '13, Dublin, Ireland - July 28 - August 01, 2013 . Google ScholarDigital Library
- Jia-Dong Zhang and Chi-Yin Chow. 2013. iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In SIGSPATIAL '13, Orlando, FL, USA, November 5--8, 2013 . Google ScholarDigital Library
- Jia-Dong Zhang and Chi-Yin Chow. 2015. GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9--13, 2015. 443--452. Google ScholarDigital Library
- Xuchao Zhang, Liang Zhao, Arnold P Boedihardjo, Chang-Tien Lu, and Naren Ramakrishnan. 2017. Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 507--516. Google ScholarDigital Library
Index Terms
- CORALS: Who Are My Potential New Customers? Tapping into the Wisdom of Customers' Decisions
Recommendations
Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data MiningRecommendation systems tend to suffer severely from the sparse training data. A large portion of users and items usually have a very limited number of training instances. The data sparsity issue prevents us from accurately understanding users' ...
Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation
Neural Information ProcessingAbstractPoint-of-interest (POI) recommendation that suggests new locations for people to visit is an important application in location-based social networks (LBSNs). Compared with traditional recommendation problems, e.g., movie recommendation, ...
Joint Heterogeneous Pair-wise Loss For Top-N Recommendation
WI '19: IEEE/WIC/ACM International Conference on Web IntelligenceWe propose a novel pairwise unified recommendation model (short for pairwise URM). The pairwise URM combines two pairwise ranking-oriented collaborative filtering approaches, namely Collaborative Less-is-More Filtering (CLiMF) and Bayesian Personal ...
Comments