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Smart Shop Services for Building Customer-Oriented Scenarios

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Języki publikacji
EN
Abstrakty
EN
The shops of today mostly support the customer by offering him or her products based on basic relationships between products viewed or ordered by users with similar tastes. This common approach may fail in many cases especially when the user does not have sufficient knowledge about the market, or when he or she wants to build a set of products in more than one shop. New categories of smart shop services are proposed in order to execute such customer-oriented scenarios where recommended products do meet mutual dependencies with products previously ordered by the customer. An attempt is made to collect additional information about the behavior of users (from past and current contexts) and represent it in a targeted graph called the customer-oriented scenario. Four types of such scenarios are distinguished depending on how many shops have been visited by the user before buying the expected products and how many products the user wants to buy. Moreover, the proposed scenario model provides the possibility of showing which services had been used by the user before the selection was made. Customer-oriented scenarios may be created post factum based on event data logs or before the user will use the shop, which means that it can be arranged which information, knowledge sources (internal or external), products or categories should be suggested in some context of the user's decision. The possibility of leveraging additional smart services into a traditional trading platform may help users, especially when they want to implement a complex scenario and order many products with mutual dependencies or in a situation when the user wants to understand the market before buying something. Using internal and external services allows creating a network for distributing knowledge focused on the actual customer context in a shop.
Rocznik
Strony
221--242
Opis fizyczny
Bibliogr. 19 poz., rys.
Twórcy
  • Department of Computer Architecture, Faculty of Electronics,Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
  • Department of Computer Architecture, Faculty of Electronics,Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
Bibliografia
  • [1] Web Page: Ecommerce conversion rates, http://www.smartinsights.com/ecommerce/ecommerce-analytics/ecommerce-conversion-rates/, Accessed on: 2017-06-14
  • [2] Web Page: Description of the Amazon Go!, https://www.amazon.com/b?node=16008589011, Accessed on: 2017-06-14
  • [3] Helms M, Ahmadi M, Jih W and Ettkin L 2008 Technologies in support of mass customization strategy: Exploring the linkages between e-commerce and knowledge management, Computers in Industry 59 (4) 351
  • [4] Garcia M, Garcia-Nieto J and Aldana-Montes J 2016 An ontology-based data integration approach for web analytics in e-commerce, Expert Systems with Applications 63 (1) 20
  • [5] Gerrikagoitia J, Castander I, Rebon F and Alzua-Sorzbal A 2015 New trends of Intelligente-marketing based on Web Mining for e-shops, Procedia-Social and Behavioral Sciences 175 (1) 75
  • [6] Erl T, Gee C, Chelliah P, Kress J, Normann H, Shuster L, Trops B, Utschig-Utschig C,Wik P and Winterberg T 2014 Next Generation SOA, Pearson Education New York
  • [7] Aloysius G and Binu D 2013 An approach to products placement in supermarkets using Prefix Span algorithm, Journal of King Saud University - Computer and InformationSciences 25 (1) 77
  • [8] Chen Y, Chen J and Tung C 2013 A data mining approach for retail knowledge discovery with considerat in of the effect of shelf-space adjacency on sales, Decision Support Systems 25 (1) 77
  • [9]Web Page: Prestashop ecommerce software, https://prestashop.com/, Accessed on:2017-06-14
  • [10]Web Page: Magento ecommerce software, https://magento.com/, Accessed on: 2017-06-14
  • [11] Philips J 2016 Ecommerce Analytics: Analyze and Improve the Impact of Your Digital Strategy, Pearson Education Inc, New Jersey
  • [12] Aggarwal C 2016 Recommender Systems, Springer New York
  • [13] Gomez-Uribe C and Hunt N 2016The netflix recommender system: Algorithms business value and innovation, ACM Transactions on Management Information Systems (TMIS) 6 (4) 13
  • [14] Akshita S 2013 Recommender System: Review, International Journal of Computer Applications 71 (42) 38
  • [15] Li H, Zhang S and Wang X 2013 A Personalization Recommendation Algorithm for E-Commerce, Journal of Software 8 (1) 176
  • [16] Web Page: Millenials description,https://www.forbes.com/sites/ajagrawal/2016/08/03/6-things-to-know-about-marketing-to-millennials, Accessed on: 2017-06-14
  • [17] Iyer R and Moorthy S 2015 Distributed Computing and Internet Technology: 11thInternational Conference ICDCIT 2015, Bhubaneswar, India, February 5-8, Springer International Publishing 233
  • [18] Sobecki A 2017PhD Thesis: The Hybrid Recommendatian System for Construction Business Application Scenarios, Gdańsk University of Technology
  • [19] Sobecki A, Szymański J, Krawczyk H, Mora H and Gil D 2020 Smart services for improving eCommerce, Theory and Applications of Dependable Computer Systems
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7803bc4c-a069-4bc2-a2c9-70e93a2021ba
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