Karimi Alavije, Mohamadreza, Askari, Shiva, Parasteh, Sirvan. (1394). Intelligent Online Store: User Behavior Analysis based Recommender System. , 7(2), 385-406. doi: 10.22059/jitm.2015.53884
Mohamadreza Karimi Alavije; Shiva Askari; Sirvan Parasteh. "Intelligent Online Store: User Behavior Analysis based Recommender System". , 7, 2, 1394, 385-406. doi: 10.22059/jitm.2015.53884
Karimi Alavije, Mohamadreza, Askari, Shiva, Parasteh, Sirvan. (1394). 'Intelligent Online Store: User Behavior Analysis based Recommender System', , 7(2), pp. 385-406. doi: 10.22059/jitm.2015.53884
Karimi Alavije, Mohamadreza, Askari, Shiva, Parasteh, Sirvan. Intelligent Online Store: User Behavior Analysis based Recommender System. , 1394; 7(2): 385-406. doi: 10.22059/jitm.2015.53884

Intelligent Online Store: User Behavior Analysis based Recommender System

Journal of Information Technology Management
مقاله 10، دوره 7، شماره 2، 2015، صفحه 385-406    اصل مقاله (570.97 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2015.53884
نویسندگان
Mohamadreza Karimi Alavije1؛ Shiva Askari* 2؛ Sirvan Parasteh3
1Assistant Prof., Business Management, Faculty of Management and Accounting University of Allameh Tabatabayi, Tehran, Iran.
2MSc Student, Business Management, Faculty of Management and Accounting, Allameh Tabatabayi University, Tehran, Iran.
3MSc, Artifiacial Intelligent, Faculty of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
چکیده
Recommender systems provide personalised recommendations to users, helping them find their ideal items, also play a key role in encouraging users to make their purchases through websites thus leading to the success of online stores. The collaborative filtering method is one of the most successful techniques utilized in these systems facilitating the provision of recommendations close to that of the customer's taste and need. However the proliferation of both customers and products on offer, the technique faces some issues such as "cold start" and scalability. As such in this paper a new method has been introduced in which user-based collaborative filtering is used at a base method along with a weighted clustering of users based upon demographics in order to improve the results obtained from the system. The implementation of the results of the algorithms demonstrate that the presented approach has a lower RMSE, which means that the system offers improved performance and accuracy and that the resulting recommendations are closer to the taste and preferences of the users.
کلیدواژه‌ها
Clustering؛ Collaborative filtering؛ demographics؛ Recommender System
مراجع
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