From Fingerprint to Footprint: Using Point of Interest (POI) Recommendation System in Marketing Applications
DOI:
https://doi.org/10.12695/ajtm.2019.12.2.4Keywords:
Digitalization, location-based social networks, user-based collaborative filtering, K-Means clustering, DBI methodAbstract
Abstract. Companies should be willing to adopt new technologies and business models to be able to stay competitive in the changing world, both regionally and globally. However, the US forest sector industry, including wood furniture sector seems to be lagging when it comes to implementing digital technologies. This study proposes a design of Point of Interest (POI) recommendation system to enhance the marketing practices to promote wood furniture stores. We produced a personal recommendation design utilising K-Means+ clustering, a combination between K-Means algorithm for spatial data clustering and Davies-Bouldin Index (DBI) methods to determine the optimal K value. This design can assist mobile users who are potential customers to find wood furniture store locations based on other users’ preferences.
Keywords: Digitalisation; location-based social networks; user-based collaborative filtering; K-Means+ clustering; DBI method
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References
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Elavarasi, S. A., & Akilandeswari, J. (2014). Occurrence based categorical data clustering using cosine and binary matching similarity measure. Journal of Theoretical and Applied Information Technology, 68(1), 208-214.
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Gazal, K., Montague, I., Poudel, R., & Wiedenbeck, J. (2016). Forest products industry in a digital age: Factors affecting social media adoption. Forest Products Journal, 66(5), 343-353.
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Hewitt, R., Sowlati, T., & Paradi, J. C. (2012). Evaluation of strategic software investments for the Canadian cabinet industry. Forest Products Journal, 62(7), 597-606.
Hewitt, R., Sowlati, T., & Paradi, J. C. (2013). Analysis of available software products in the North American cabinet industry. International Journal of Applied Management Science, 5(3), 281-296. DOI: 10.1504/IJAMS.2013.055437
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Jahiruzzaman, M., & Hossain, A. A. (2015). Detection and classification of diabetic retinopathy using K-means clustering and fuzzy logic. 18th International Conference on Computer and Information Technology (ICCIT), pp. 534-538. DOI: 10.1109/ICCITechn.2015.7488129
Jiang, S., Qian, X., Mei, T., & Fu, Y. (2016). Personalized travel sequence recommendation on multi-source big social media. IEEE Transactions on Big Data, 2(1), 43-56. DOI: 10.1109/TBDATA.2016.2541160
Katarya, R., & Verma, O. P. (2017). An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal, 18(2), 105-112. DOI: 10.1016/j.eij.2016.10.002
Kesorn, K., Juraphanthong, W., & Salaiwarakul, A. (2017). Personalized attraction recommendation system for tourists through check-in data. IEEE Access, 5, 26703-26721. DOI: 10.1109/ACCESS.2017.2778293
Kim, H. K., Kim, J. K., & Ryu, Y. U. (2009). Personalized recommendation over a customer network for ubiquitous shopping. IEEE Transactions on Services Computing, 2(2), 140-151. DOI: 10.1109/TSC.2009.7
Kozak, R. A. (2002). Internet readiness and e-business adoption of Canadian value-added wood producers. The Forestry Chronicle, 78(2), 296-305. DOI: 10.5558/tfc78296-2
Larasatie, P. (2018). Indonesian Furniture Producers: Change Makers or Change Takers? BioProducts Business, 3(4), 39-50. DOI: 10.22382/bpb-2018-004
Makkonen, M. (2018). Stakeholder perspectives on the business potential of digitalization in the wood products industry. BioProducts Business, 3(6), 63–80.
Martin, B. T. (2009). Opportunities for an Online GIS-Based Wood Supply Management System. Doctoral dissertation, Virginia Tech.
Matt, C., Hess, T., & Benlian, A. (2015). Digital transformation strategies. Business & Information Systems Engineering, 57(5), 339-343.
Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650-1654.
McKercher, B. (2016). Towards a taxonomy of tourism products. Tourism Management, 54, 196-208. DOI: 10.1016/j.tourman.2015.11.008
Montague, I. B. (2011). Social network media in the forest products industry: A look at a new way of marketing. The 3rd International Scientific Conference on Hardwood Processing, pp. 235–241.
Montague, I., Gazal, K. A., Wiedenbeck, J., & Shepherd, J. G. (2016). Forest products industry in a digital age: A look at e-commerce and social media. Forest Products Journal, 66(1), 49-57.
Montague, I. B., Gazal, K. A., & Wiedenbeck, J. K. (2019). Social media use in the wood products industry: Impact on the consumer purchasing process. BioProducts Business, 4(3), 27–40.
Odić, A., Tkalčič, M., Tasič, J. F., & Košir, A. (2013). Predicting and detecting the relevant contextual information in a movie-recommender system. Interacting with Computers, 25(1), 74-90. DOI: 10.1093/iwc/iws003
Parviainen, P., Tihinen, M., Kääriäinen, J., & Teppola, S. (2017). Tackling the digitalization challenge: How to benefit from digitalization in practice. International Journal of Information Systems and Project Management, 5(1), 63-77.
Pitis, O. T., & Vlosky, R. P. (2000). Forest products exporting and the Internet: Current use figures and implementation issues. Forest Products Journal, 50(10), 23-29.
Poku, K. (2003). Impact of corporate orientation of information technology adoption in the United States forest products industry. Doctoral dissertation, Louisiana State University.
Quesada-Pineda, H., Brenes-Bastos, M., & Smith, R. (2017). Assessing geographic information systems use in marketing applications for the wood products industry. BioProducts Business, 2(1), 14–22.
Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender systems handbook. Second edition. New York: Springer.
Sanchez, F., Alduán, M., Alvarez, F., Menéndez, J. M., & Baez, O. (2012). Recommender system for sport videos based on user audiovisual consumption. IEEE Transactions on Multimedia, 14(6), 1546-1557. DOI: 10.1109/TMM.2012.2217121
Shook, S. R., Zhang, Y., Braden, R., & Baldridg, J. (2002). Pacific Northwest secondary forest products industry. Forest Products Journal, 52(1), 59–66.
Sitompul, B. J. D. (2018). Peningkatan hasil evaluasi clustering davies-bouldin index dengan penentuan titik pusat cluster awal algoritma k-means (Indonesian). Master Thesis. Sumatera Utara University.
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Stennes, B., Stonestreet, C., Wilson, B., & Wang, S. (2006). E-technology adoption by value added wood processors in British Columbia. Forest Products Journal, 56 (5), 24–28.
Sundar, C., Chitradevi, M., & Geetharamani, G. (2012). An analysis on the performance of k-means clustering algorithm for cardiotocogram data clustering. International Journal on Computational Sciences & Applications (IJCSA), 2(5), 11-20.
Toivonen, R. (1999). Planning the Use of Information Technology in Marketing: The Case of Finnish Forest Industries. Forest Products Journal, 49(10), 25–30.
Trang, S. T. N., Zander, S., de Visser, B., & Kolbe, L. M. (2016). Towards an importance–performance analysis of factors affecting e-business diffusion in the wood industry. Journal of Cleaner Production, 110, 121–31. DOI: 10.1016/j.jclepro.2015.05.051
Tuan, C. C., Hung, C. F., & Wu, Z. H. (2017). Collaborative location recommendations with dynamic time periods. Pervasive and Mobile Computing, 35, 1-14. DOI: 10.1016/j.pmcj.2016.07.008
Vlosky, R. P. (1999). eBusiness in the Forest Products Industry. Forest Products Journal, 49(10), 12–21.
Vlosky, R. P., & Smith, T. M. (2003). eBusiness in the US hardwood lumber industry. Forest Products Journal, 53(5), 21-29.
Vlosky, R. P., & Westbrook, T. (2002). eBusiness exchange between homecenter buyers and wood products suppliers. Forest Products Journal, 52(1), 38–43.
Vlosky, R. P., Westbrook, T., & Poku, K. (2002). An exploratory study of Internet adoption by primary wood products manufacturers in the western United States. Forest Products Journal, 52(6), 35-42.
Vlosky, R. P., & Youn, Y. C. (2002). A Cross-National Study of Internet Adoption in the Forest Products Industry in the United States and South Korea. Journal Korean For. Soc., 91(2), 182–92.
Zahra, S., Ghazanfar, M. A., Khalid, A., Azam, M. A., Naeem, U., & Prugel-Bennett, A. (2015). Novel centroid selection approaches for KMeans-clustering based recommender systems. Information Sciences, 320, 156-189. DOI: 10.1016/j.ins.2015.03.062
Zander, S., Trang, S. T. N., Mandrella, M., Marrone, M., & Kolbe, L. M. (2015). Integrating Industry Characteristics in Inter-Organizational IS Adoption Models: A Mixed Method Approach. Pacific Asia Conference on Information Systems.
Yaacob, R., & Baroto, M. B. (2019). Influencing Variables Towards the Intention to Purchase (ITP) and Their Research Gaps. The Asian Journal of Technology Management, 12(1), 1-14.
Zhang, X., Jia, J., Gao, K., Zhang, Y., Zhang, D., Li, J., & Tian, Q. (2017). Trip outfits advisor: Location-oriented clothing recommendation. IEEE Transactions on Multimedia, 19(11), 2533-2544. DOI: 10.1109/TMM.2017.2696825
Zhang, F. (2016). A personalized time-sequence-based book recommendation algorithm for digital libraries. IEEE Access, 4, 2714-2720. DOI: 10.1109/ACCESS.2016.2564997
Zzauer. 2017. “Digitalization requires a radical change in organizational culture.” Capgemini Worldwide (blog). Available from https://www.capgemini.com/2017/06/digitalisation-requires-a-radical-change-in-organisational-culture/ [accessed 13 July 2019]
Adomavicius, G., Mobasher, B., Ricci, F., & Tuzhilin, A. (2011). Context-aware recommender systems. AI Magazine, 32(3), 67-80. DOI: 10.1609/aimag.v32i3.2364
AF&PA. (2018). Economic impact. Available from http://www.afandpa.org/our-industry/economic-impact [accessed 13 July 2019]
Aggarwal, C. C. (2016). An introduction to recommender systems. Cham: Springer International Publishing.
Arano, K. G., & Spong, B. (2012). Electronic commerce adoption in the hardwood industry. Journal of Extension, 50(6).
Beier, G., Niehoff, S., Ziems, T., & Xue, B. (2017). Sustainability aspects of a digitalized industry–A comparative study from China and Germany. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(2), 227-234.
Chen, H. C., & Chen, A. L. (2005). A music recommendation system based on music and user grouping. Journal of Intelligent Information Systems, 24(2-3), 113-132.
Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227.
Dupuy, C. A., & Vlosky, R. P. (2000). Status of electronic data interchange in the forest products industry. Forest Products Journal, 50(6), 32–38.
Elavarasi, S. A., & Akilandeswari, J. (2014). Occurrence based categorical data clustering using cosine and binary matching similarity measure. Journal of Theoretical and Applied Information Technology, 68(1), 208-214.
Gazal, K. A., Montague, I., & Wiedenbeck, J. (2019). Factors affecting social media adoption among wood products consumers. BioProducts Business, 4(5), 51–62.
Gazal, K., Montague, I., Poudel, R., & Wiedenbeck, J. (2016). Forest products industry in a digital age: Factors affecting social media adoption. Forest Products Journal, 66(5), 343-353.
Goodwin, T. (2015). The battle is for the customer interface. TechCrunch (blog). Available from http://social.techcrunch.com/2015/03/03/in-the-age-of-disintermediation-the-battle-is-all-for-the-customer-interface/ [accessed 13 July 2019]
Hewitt, R., Sowlati, T., & Paradi, J. C. (2011). Information technology adoption in US and Canadian forest products industries. Forest Products Journal, 61(2), 161-169.
Hewitt, R., Sowlati, T., & Paradi, J. C. (2012). Evaluation of strategic software investments for the Canadian cabinet industry. Forest Products Journal, 62(7), 597-606.
Hewitt, R., Sowlati, T., & Paradi, J. C. (2013). Analysis of available software products in the North American cabinet industry. International Journal of Applied Management Science, 5(3), 281-296. DOI: 10.1504/IJAMS.2013.055437
Hu, X., Li, X., Ngai, E. C. H., Leung, V. C., & Kruchten, P. (2014). Multidimensional context-aware social network architecture for mobile crowdsensing. IEEE Communications Magazine, 52(6), 78-87. DOI: 10.1109/MCOM.2014.6829948
Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273. DOI: 10.1016/j.eij.2015.06.005
Jahiruzzaman, M., & Hossain, A. A. (2015). Detection and classification of diabetic retinopathy using K-means clustering and fuzzy logic. 18th International Conference on Computer and Information Technology (ICCIT), pp. 534-538. DOI: 10.1109/ICCITechn.2015.7488129
Jiang, S., Qian, X., Mei, T., & Fu, Y. (2016). Personalized travel sequence recommendation on multi-source big social media. IEEE Transactions on Big Data, 2(1), 43-56. DOI: 10.1109/TBDATA.2016.2541160
Katarya, R., & Verma, O. P. (2017). An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal, 18(2), 105-112. DOI: 10.1016/j.eij.2016.10.002
Kesorn, K., Juraphanthong, W., & Salaiwarakul, A. (2017). Personalized attraction recommendation system for tourists through check-in data. IEEE Access, 5, 26703-26721. DOI: 10.1109/ACCESS.2017.2778293
Kim, H. K., Kim, J. K., & Ryu, Y. U. (2009). Personalized recommendation over a customer network for ubiquitous shopping. IEEE Transactions on Services Computing, 2(2), 140-151. DOI: 10.1109/TSC.2009.7
Kozak, R. A. (2002). Internet readiness and e-business adoption of Canadian value-added wood producers. The Forestry Chronicle, 78(2), 296-305. DOI: 10.5558/tfc78296-2
Larasatie, P. (2018). Indonesian Furniture Producers: Change Makers or Change Takers? BioProducts Business, 3(4), 39-50. DOI: 10.22382/bpb-2018-004
Makkonen, M. (2018). Stakeholder perspectives on the business potential of digitalization in the wood products industry. BioProducts Business, 3(6), 63–80.
Martin, B. T. (2009). Opportunities for an Online GIS-Based Wood Supply Management System. Doctoral dissertation, Virginia Tech.
Matt, C., Hess, T., & Benlian, A. (2015). Digital transformation strategies. Business & Information Systems Engineering, 57(5), 339-343.
Maulik, U., & Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 1650-1654.
McKercher, B. (2016). Towards a taxonomy of tourism products. Tourism Management, 54, 196-208. DOI: 10.1016/j.tourman.2015.11.008
Montague, I. B. (2011). Social network media in the forest products industry: A look at a new way of marketing. The 3rd International Scientific Conference on Hardwood Processing, pp. 235–241.
Montague, I., Gazal, K. A., Wiedenbeck, J., & Shepherd, J. G. (2016). Forest products industry in a digital age: A look at e-commerce and social media. Forest Products Journal, 66(1), 49-57.
Montague, I. B., Gazal, K. A., & Wiedenbeck, J. K. (2019). Social media use in the wood products industry: Impact on the consumer purchasing process. BioProducts Business, 4(3), 27–40.
Odić, A., Tkalčič, M., Tasič, J. F., & Košir, A. (2013). Predicting and detecting the relevant contextual information in a movie-recommender system. Interacting with Computers, 25(1), 74-90. DOI: 10.1093/iwc/iws003
Parviainen, P., Tihinen, M., Kääriäinen, J., & Teppola, S. (2017). Tackling the digitalization challenge: How to benefit from digitalization in practice. International Journal of Information Systems and Project Management, 5(1), 63-77.
Pitis, O. T., & Vlosky, R. P. (2000). Forest products exporting and the Internet: Current use figures and implementation issues. Forest Products Journal, 50(10), 23-29.
Poku, K. (2003). Impact of corporate orientation of information technology adoption in the United States forest products industry. Doctoral dissertation, Louisiana State University.
Quesada-Pineda, H., Brenes-Bastos, M., & Smith, R. (2017). Assessing geographic information systems use in marketing applications for the wood products industry. BioProducts Business, 2(1), 14–22.
Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender systems handbook. Second edition. New York: Springer.
Sanchez, F., Alduán, M., Alvarez, F., Menéndez, J. M., & Baez, O. (2012). Recommender system for sport videos based on user audiovisual consumption. IEEE Transactions on Multimedia, 14(6), 1546-1557. DOI: 10.1109/TMM.2012.2217121
Shook, S. R., Zhang, Y., Braden, R., & Baldridg, J. (2002). Pacific Northwest secondary forest products industry. Forest Products Journal, 52(1), 59–66.
Sitompul, B. J. D. (2018). Peningkatan hasil evaluasi clustering davies-bouldin index dengan penentuan titik pusat cluster awal algoritma k-means (Indonesian). Master Thesis. Sumatera Utara University.
Solis, B. (2014). Digital transformation and the race against digital. Available from https://www.prophet.com/2014/09/digital-transformation-and-the-race-against-digital-darwinism/ [accessed 13 July 2019]
Stennes, B., Stonestreet, C., Wilson, B., & Wang, S. (2006). E-technology adoption by value added wood processors in British Columbia. Forest Products Journal, 56 (5), 24–28.
Sundar, C., Chitradevi, M., & Geetharamani, G. (2012). An analysis on the performance of k-means clustering algorithm for cardiotocogram data clustering. International Journal on Computational Sciences & Applications (IJCSA), 2(5), 11-20.
Toivonen, R. (1999). Planning the Use of Information Technology in Marketing: The Case of Finnish Forest Industries. Forest Products Journal, 49(10), 25–30.
Trang, S. T. N., Zander, S., de Visser, B., & Kolbe, L. M. (2016). Towards an importance–performance analysis of factors affecting e-business diffusion in the wood industry. Journal of Cleaner Production, 110, 121–31. DOI: 10.1016/j.jclepro.2015.05.051
Tuan, C. C., Hung, C. F., & Wu, Z. H. (2017). Collaborative location recommendations with dynamic time periods. Pervasive and Mobile Computing, 35, 1-14. DOI: 10.1016/j.pmcj.2016.07.008
Vlosky, R. P. (1999). eBusiness in the Forest Products Industry. Forest Products Journal, 49(10), 12–21.
Vlosky, R. P., & Smith, T. M. (2003). eBusiness in the US hardwood lumber industry. Forest Products Journal, 53(5), 21-29.
Vlosky, R. P., & Westbrook, T. (2002). eBusiness exchange between homecenter buyers and wood products suppliers. Forest Products Journal, 52(1), 38–43.
Vlosky, R. P., Westbrook, T., & Poku, K. (2002). An exploratory study of Internet adoption by primary wood products manufacturers in the western United States. Forest Products Journal, 52(6), 35-42.
Vlosky, R. P., & Youn, Y. C. (2002). A Cross-National Study of Internet Adoption in the Forest Products Industry in the United States and South Korea. Journal Korean For. Soc., 91(2), 182–92.
Zahra, S., Ghazanfar, M. A., Khalid, A., Azam, M. A., Naeem, U., & Prugel-Bennett, A. (2015). Novel centroid selection approaches for KMeans-clustering based recommender systems. Information Sciences, 320, 156-189. DOI: 10.1016/j.ins.2015.03.062
Zander, S., Trang, S. T. N., Mandrella, M., Marrone, M., & Kolbe, L. M. (2015). Integrating Industry Characteristics in Inter-Organizational IS Adoption Models: A Mixed Method Approach. Pacific Asia Conference on Information Systems.
Yaacob, R., & Baroto, M. B. (2019). Influencing Variables Towards the Intention to Purchase (ITP) and Their Research Gaps. The Asian Journal of Technology Management, 12(1), 1-14.
Zhang, X., Jia, J., Gao, K., Zhang, Y., Zhang, D., Li, J., & Tian, Q. (2017). Trip outfits advisor: Location-oriented clothing recommendation. IEEE Transactions on Multimedia, 19(11), 2533-2544. DOI: 10.1109/TMM.2017.2696825
Zhang, F. (2016). A personalized time-sequence-based book recommendation algorithm for digital libraries. IEEE Access, 4, 2714-2720. DOI: 10.1109/ACCESS.2016.2564997
Zzauer. 2017. “Digitalization requires a radical change in organizational culture.” Capgemini Worldwide (blog). Available from https://www.capgemini.com/2017/06/digitalisation-requires-a-radical-change-in-organisational-culture/ [accessed 13 July 2019]
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Submitted
2019-07-30
Accepted
2019-08-21
Published
2019-08-28
How to Cite
Larasatie, P., & Setiowati, S. (2019). From Fingerprint to Footprint: Using Point of Interest (POI) Recommendation System in Marketing Applications. The Asian Journal of Technology Management (AJTM), 12(2), 118–131. https://doi.org/10.12695/ajtm.2019.12.2.4
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