Recommending Stores for Shopping Mall Customers with RecStore
DOI:
https://doi.org/10.5753/jidm.2018.2040Keywords:
customization, mall, mobile, recommendation, shoppingAbstract
Today mobility is a key feature in the new generation of Internet, which provides a set of custom services through numerous terminals. Smartphones, for example, are a tendency and almost mandatory for anyone living in an urban and modern context. Most developed cities have at least one shopping mall full of mobile device users. These shopping malls provide a number of stores, and people tend to have difficult in finding what they really need. This article proposes a solution called RecStore. RecStore is a recommendation model to assist customers in reaching what they consider relevant at malls. The recommendation model comprises user activities, 330 stores, 30 users and 3 baseline models. The precision, recall and f-measure improved at rates of 118%, 76% and 95% respectively in comparison to the second best model of each metric. Additionally, a mobile application — called InMap — was implemented based on our model RecStore.
Downloads
References
Anacleto, R., Luz, N., Almeida, A., Figueiredo, L., and Novais, P. Shopping center tracking and recommendation systems. In Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Springer, pp. 299–308, 2011.
Asthana, A., Cravatts, M., and Krzyzanowski, P. An indoor wireless system for personalized shopping assistance. In Mobile Computing Systems and Applications, 1994. Proceedings., Workshop on. IEEE, pp. 69–74, 1994.
Baeza-Yates, R. and Ribeiro-Neto, B. Modern Information Retrieval. Addison Wesley, 1999.
Bajo, J., Corchado, J. M., De Paz, Y., De Paz, J. F., Rodríguez, S., Martín, Q., and Abraham, A. Shomas: Intelligent guidance and suggestions in shopping centres. Applied Soft Computing 9 (2): 851–862, 2009.
Bohnenberger, T., Jameson, A., Krüger, A., and Butz, A. Location-aware shopping assistance: Evaluation of a decision-theoretic approach. In Human Computer Interaction with Mobile Devices. Springer, pp. 155–169, 2002.
Brusilovsky, P. Adaptive hypermedia. User Modeling and User-Adapted Interaction 11 (1-2): 87–110, Mar., 2001.
Chang Lee, K. and Chung, N. A web dss approach to building an intelligent internet shopping mall by integrating virtual reality and avatar. Expert Syst. Appl. 28 (2): 333–346, Feb., 2005.
Church, K. and Smyth, B. Who, what, where & when: A new approach to mobile search. In Proceedings of the 13th International Conference on Intelligent User Interfaces. IUI ’08. ACM, New York, NY, USA, pp. 309–312, 2008.
Fang, B., Liao, S., Xu, K., Cheng, H., Zhu, C., and Chen, H. A novel mobile recommender system for indoor shopping. Expert Systems with Applications 39 (15): 11992–12000, 2012.
Hennig-Thurau, T. and Klee, A. The impact of customer satisfaction and relationship quality on customer retention: A critical reassessment and model development. Psychology & Marketing 14 (8): 737–764, 1997.
Holding, N. Nielsen ibope, 2015.
Huang, H., Zhao, B., Zhao, H., Zhuang, Z., Wang, Z., Yao, X., Wang, X., Jin, H., and Fu, X. A crossplatform consumer behavior analysis of large-scale mobile shopping data. In Proceedings of the 2018 World Wide Web Conference. WWW ’18. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 1785–1794, 2018.
Hussein, G. Mobile recommender system analysis and design. 2009 1st International Conference on Networked Digital Technologies, NDT 2009 , 08, 2009.
Kim, K.-J. and Kim, B.-G. Product recommender system for online shopping malls using data mining techniques. Journal of Intelligence and Information Systems vol. 11, 01, 2005.
Kim, T. and Park, S. C. One-to-one customized brand recommendation in virtual shopping mall. In Management of Innovation and Technology, 2000. ICMIT 2000. Proceedings of the 2000 IEEE International Conference on. Vol. 2. pp. 659–663 vol.2, 2000.
Kramer and Modsching, R. Context-aware adaptation in a mobile tour guide. Proc. of the 5th International and Interdisciplinary Conference on Modeling and Using Context, 2005.
Lemos, F. and Goes, L. F. Evaluation of consumer shopping behavior in m-commerce and e-commerce. In Proceedings of the Annual Conference on Brazilian Symposium on Information Systems: Information Systems: A Computer Socio-Technical Perspective - Volume 1. SBSI 2015. Brazilian Computer Society, Porto Alegre, Brazil, Brazil, pp. 18:127–18:134, 2015.
Marinho, L. B., Nunes, I., Sandholm, T., Nóbrega, C., Araújo, J. a., and Pires, C. E. S. Improving location recommendations with temporal pattern extraction. In Proceedings of the 18th Brazilian Symposium on Multimedia and the Web. WebMedia ’12. ACM, New York, NY, USA, pp. 293–296, 2012.
Martin, B. and Mason, S. Current trends in leisure. Leisure Studies vol. 6, pp. 93–97, 1987.
Martins, E. F., Belém, F. M., Almeida, J. M., and Gonçalves, M. Measuring and addressing the impact of cold start on associative tag recommenders. In Proceedings of the 19th Brazilian Symposium on Multimedia and the Web. WebMedia ’13. ACM, New York, NY, USA, pp. 325–332, 2013.
Miller, D. A theory of shopping. Cornell University Press, 1998.
Pessemier, T. D., Deryckere, T., and Martens, L. Extending the bayesian classifier to a context-aware recommender system for mobile devices. In 2010 Fifth International Conference on Internet and Web Applications and Services. pp. 242–247, 2010.
Picault, J., Ribière, M., Bonnefoy, D., and Mercer, K. How to get the recommender out of the lab? In Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor (Eds.). Springer US, pp. 333–365, 2011.
Ricci, F. Mobile recommender systems. Information Technology & Tourism 12 (3): 205–231, 2010.
Robusto, C. C. The cosine-haversine formula. The American Mathematical Monthly 64 (1): 38–40, 1957.
Sae-Ueng, S., Pinyapong, S., Ogino, A., and Kato, T. Consumer-friendly shopping assistance by personal behavior log analysis on ubiquitous shop space, 2008.
Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A., and Bernier, C. Analysis of strategies for building group profiles. In User Modeling, Adaptation, and Personalization, P. De Bra, A. Kobsa, and D. Chin (Eds.). Lecture Notes in Computer Science, vol. 6075. Springer Berlin / Heidelberg, pp. 40–51, 2010.
Silva, D. V. d. S., Silva, R. d. S., and Durão, F. A. Recstore: Recommending stores for shopping mall customers. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. https://doi.org/10.1145/3126858.3126888. WebMedia ’17. ACM, New York, NY, USA, pp. 117–124, 2017.
Stahl, C., Baus, J., Brandherm, B., Schmitz, M., and Schwartz, T. Navigational-and shopping assistance on the basis of user interactions in intelligent environments, 2005.
Vincenty, T. Direct and inverse soluctions of geodesics on the ellipsoid with application of nested equations. Directorate of Overseas Surveys 23 (176): 89–90, 1975.
Von Reischach, F., Guinard, D., Michahelles, F., and Fleisch, E. A mobile product recommendation system interacting with tagged products. In Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on. IEEE, pp. 1–6, 2009.
Yang, W.-S., Cheng, H.-C., and Dia, J.-B. A location-aware recommender system for mobile shopping environments. Expert Systems with Applications 34 (1): 437–445, 2008.
Zhu, H. and Tian, E. C. H. X. K. Y. H. C. J. Mining mobile user preferences for personalized context-aware recommendation. ACM Trans. Program. Lang. Syst., December, 2014.
