近年來，適地性服務(Location-based Service)搭上行動網路普及的風潮，於企業而言已成為一行銷的重要利器。於零售業者，零售店面位置之選擇與適當的行銷手法，能大幅度的增加產品曝光度及獲利。但零售店面位置選擇之議題，在分析資料之蒐集上著實困難，故本研究利用適地性社群網路資料，透過分析使用者足跡與興趣點之關聯性，發展一個零售點推薦之方法。
透過目標區域關聯性分析與人群移動模式萃取，本研究建立零售點推薦之特徵值(Features)。本研究設計興趣點間之產業密度(Density)、區域類別(Category)與區域/產業分群(Clustering)，以及區域飽和度的計算，以核密度估計(Kernel Density Estimation, KDE)、k-平均演算法(k-Means)等方法，計算區域關聯性對零售點選擇之影響。另外，本研究建立人群移動模式擷取方法，以循序模式探勘(Sequential Pattern Mining)方法選取最常行動之路徑，以量化人群行動對候選零售點之影響力。本研究分別對興趣點分析與行動路徑產生多個特徵值，並採以排名品質指標(Nomalized Discounted Cumulative Gain, NDCG)衡量多個特徵值之有效性。最後，本研究使用Foursquare與Facebook的資料集進行零售點推薦之實驗，以CART決策樹(Classification and Regression Tree)演算法進行零售點推薦。在本研究所萃取的特徵值中部分表現相當優異，且建立了以適地性服務進行零售點推薦的方法，並實際以台南地區的店面進行推薦。

In recent years, with the popularization of mobile network, the location-based service (LBS) has made great strides, becoming an efficient marketing instrument for enterprises. For the retail business, good selections of store and appropriate marketing techniques are critical to increasing the profit. However, it is difficult to select the retail store because there are numerous considerations and the analysis was short of metadata in the past. Therefore, this study uses LBS, and provides a recommendation method for retail store selection by analyzing the relationship between the user track and point-of-interest (POI).
This study uses regional relevance and human mobility analysis to establish the feature values of retail store recommendation. This study proposes (1) architecture of the data model available for retail store recommendation by influential layers of LBS, and System-based solution for recommendation of retail stores; (2) Industry density, area categories and region/industry clustering methods of POIs. Uses KDE and KMeans to calculate the effect of regional functionality on the retail store selection; (3) Provide a mobility (sequence) feature extraction method to measure mobility track. Finally, give an evaluation and implementation for the result.

Akalin, M., Turhan, G., & Sahin, A. (2016). The application of AHP approach for evaluating location selection elements for retail store: a case of clothing store. International Journal of Research in Business and Social Science (2147-4478), 2(4), 1-20.
Bao, J., Zheng, Y., & Mokbel, M. F. (2012, November). Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th international conference on advances in geographic information systems (pp. 199-208). ACM.
Blanke, U., Tröster, G., Franke, T., & Lukowicz, P. (2014, April). Capturing crowd dynamics at large scale events using participatory gps-localization. In Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference on (pp. 1-7). IEEE.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press.
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005, August). Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (pp. 89-96). ACM.
Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009, November). Expected reciprocal rank for graded relevance. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 621-630). ACM.
Chen, L. F., & Tsai, C. T. (2016). Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain. Tourism Management, 53, 197-206.
Chorley, M. J., Whitaker, R. M., & Allen, S. M. (2015). Personality and location-based social networks. Computers in Human Behavior, 46, 45-56.
Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., & Riedl, J. (2003, April). Is seeing believing?: how recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 585-592). ACM.
Dhar, S., & Varshney, U. (2011). Challenges and business models for mobile location-based services and advertising. Communications of the ACM, 54(5), 121-128.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, pp. 241-249). New York: Springer series in statistics.
Fu, Y., Ge, Y., Zheng, Y., Yao, Z., Liu, Y., Xiong, H., & Yuan, J. (2014, December). Sparse real estate ranking with online user reviews and offline moving behaviors. In Data Mining (ICDM), 2014 IEEE International Conference on (pp. 120-129). IEEE.
Fu, Y., Xiong, H., Ge, Y., Yao, Z., Zheng, Y., & Zhou, Z. H. (2014, August). Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1047-1056). ACM.
Ghosh, A., & Craig, C. S. (1983). Formulating retail location strategy in a changing environment. The Journal of Marketing, 56-68.
Giannotti, F., Nanni, M., Pinelli, F., & Pedreschi, D. (2007, August). Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 330-339). ACM.
Hernandez, T., & Bennison, D. (2000). The art and science of retail location decisions. International Journal of Retail & Distribution Management, 28(8), 357-367.
Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 20(4), 422-446.
Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., & Mascolo, C. (2013, August). 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 (pp. 793-801). ACM.
Li, Y. M., Chou, C. L., & Lin, L. F. (2014). A social recommender mechanism for location-based group commerce. Information Sciences, 274, 125-142.
Liu, T. Y. (2009). Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval, 3(3), 225-331.
Nilsson, I. M., & Smirnov, O. A. (2016). Clustering vs. relative location: Measuring spatial interaction between retail outlets. Papers in Regional Science.
Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers. John Wiley & Sons.
O'Kelly, M. (2001). Retail market share and saturation. Journal of Retailing and Consumer Services, 8(1), 37-45.
Roig-Tierno, N., Baviera-Puig, A., Buitrago-Vera, J., & Mas-Verdu, F. (2013). The retail site location decision process using GIS and the analytical hierarchy process. Applied Geography, 40, 191-198.
Sanakulov, N., & Karjaluoto, H. (2015). Consumer adoption of mobile technologies: a literature review. International Journal of Mobile Communications, 13(3), 244-275.
Scott, D. W. (2015). Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons.
Silverman, B. W. (1986). Density estimation for statistics and data analysis (Vol. 26). CRC press.
Srikant, R., & Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. Advances in Database Technology—EDBT'96, 1-17.
Sun, Y., Wang, N., Shen, X. L., & Zhang, J. X. (2015). Location information disclosure in location-based social network services: Privacy calculus, benefit structure, and gender differences. Computers in Human Behavior, 52, 278-292.
Wand, M. P., & Jones, M. C. (1994). Kernel smoothing. Crc Press.
Wang, L., Gopal, R., Shankar, R., & Pancras, J. (2015). On the brink: Predicting business failure with mobile location-based checkins. Decision Support Systems, 76, 3-13.
Wen, Y. T., Lei, P. R., Peng, W. C., & Zhou, X. F. (2014, December). Exploring social influence on location-based social networks. In 2014 IEEE International Conference on Data Mining (pp. 1043-1048). IEEE.
Wei, L. Y., Zheng, Y., & Peng, W. C. (2012, August). Constructing popular routes from uncertain trajectories. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 195-203). ACM.
Wikipedia, (2017a), CartoDB. Retrieved June 1, 2017, from https://en.wikipedia.org/wiki/CartoDB .
Wikipedia, (2017b), Facebook. Retrieved May 25, 2017, from https://en.wikipedia.org/wiki/Facebook .
Wikipedia, (2017c), Foursquare. Retrieved May 25, 2017, from https://en.wikipedia.org/wiki/Foursquare .
Wikipedia, (2017d), Gowalla. Retrieved May 25, 2017, from https://en.wikipedia.org/wiki/Gowalla .
Wikipedia, (2017e), Weka_(machine_learning). Retrieved May 26, 2017, from https://en.wikipedia.org/wiki/Weka_(machine_learning) .Wilson, M. W. (2012). Location-based services, conspicuous mobility, and the location-aware future. Geoforum, 43(6), 1266-1275.
Yang, D., Zhang, D., Zheng, V. W., & Yu, Z. (2015). Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1), 129-142.
Yang, D., Zhang, D., Chen, L., & Qu, B. (2015). NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs. Journal of Network and Computer Applications, 55, 170-180.
Ye, M., Yin, P., Lee, W. C., & Lee, D. L. (2011, July). Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (pp. 325-334). ACM.
Ying, J. J. C., Lu, E. H. C., Kuo, W. N., & Tseng, V. S. (2012, August). Urban point-of-interest recommendation by mining user check-in behaviors. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing (pp. 63-70). ACM.
Yuan, J., Zheng, Y., & Xie, X. (2012, August). Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 186-194). ACM.
Zhang, J. D., Chow, C. Y., & Li, Y. (2014, November). LORE: exploiting sequential influence for location recommendations. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 103-112). ACM.
Zhang, J. D., & Chow, C. Y. (2015). CoRe: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations. Information Sciences, 293, 163-181.
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), 38.
Zheng, Y. & Xie, X. (2011). Location-Based Social Networks - Microsoft Research. Retrieved May 25, 2017, from http://research.microsoft.com/en-us/projects/lbsn/.

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