Customer Behaviour Analysis for Recommendation of Supermarket Ware

Abstract : In this paper, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon Company, both containing information about customers’ purchases. Subsequently, our model analyzes these data in order to classify customers as well as products; whereas being trained and validated with real data. This model is targeted towards classifying customers according to their consuming behaviour and consequently propose new products more likely to be purchased by them. The corresponding prediction model is intended to be utilized as a tool for marketers so as to provide an analytically targeted and specified consumer behavior.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.471-480, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_41〉
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Stavros Iakovou, Andreas Kanavos, Athanasios Tsakalidis. Customer Behaviour Analysis for Recommendation of Supermarket Ware. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.471-480, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_41〉. 〈hal-01557637〉

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