ORIGINAL RESEARCH Framework of dynamic recommendation system for e-shopping Shihab Uddin Tareq 1 • Md. Habibullah Noor 1 • Chinmay Bepery 2 Received: 20 February 2018 / Accepted: 7 November 2019 Ó Bharati Vidyapeeth’s Institute of Computer Applications and Management 2019 Abstract The popularity of online shopping is growing rapidly in modern virtual market. Generally, customers take decision to purchase goods based on their basic need and relative need. Shopkeepers play an important role to influence the customers in real market. Recommendation engine is nothing but a good automated shopkeeper. In this paper, we propose a model of dynamic recommendation system (DRS) for online market. Our proposed technique provides an intelligent solution model to overcome the problems of customers’ rating and their feedback by inte- grating market basket analysis, frequent item mining, bestselling items and customer personalization. Keywords Recommendation Á Collaborative filtering Á Content based filtering Á Data sparsity Á Scalability Á Demographic Á Robustness Á Serendipity 1 Introduction People are familiar with recommendation system from ancient age. Computerized recommendation system was first introduced by ‘‘Jussi karlgren’’ in a technical report [1, 2]. Generally, people depend on recommendations delivered by another people through their words and activities [3, 4]. In real market, when customers ask for anything seller shows not only the demanded product but also shows some other similar products that customer may want. But in virtual market, there is opportunity to do this. Now-a-days in the context of online shopping, few online shops use attaining some sort of recommendation. If we create and implement it properly, it may boost up income significantly. We know that when we buy a product from online shop, they suggest us for rating their product. As this is not a mandatory field, majority skip this option. From literature, existing recommendation system recommends a product based on user rating. But there are some problems in the user rating-based recommendation system like data sparsity [5, 6], scalability [7, 8], shilling attack [9, 10]. 1.1 Data sparsity The sparsity come in a variety of state, especially the cold start problem occurs when new user or item has just come in the device, it is tough to give recommendation ones because there is not enough information. New items shouldn’t be suggested until some users rate it [5, 6]. 1.2 Scalability When the user and item increase tremendously in the system then it is so difficult to make recommendation. Because in the collaborative filtering sustain serious & Md. Habibullah Noor habib.cse.pstu@gmail.com Shihab Uddin Tareq tareq.jkt94@gmail.com Chinmay Bepery chinmay.cse@gmail.com 1 Department of Computer Science and Engineering, Patuakhali Science and Technology University, Patuakhali, Bangladesh 2 Department of Computer Science and Information Technology, Faculty of Computer Science and Engineering, Patuakhali Science and Technology University, Patuakhali, Bangladesh 123 Int. j. inf. tecnol. https://doi.org/10.1007/s41870-019-00388-6