NDLI: An approach for personalization of banking services in multi-channel environment using memory-based collaborative filtering

Content Provider	IEEE Xplore Digital Library
Author	Abdollahpouri, H. Abdollahpouri, A.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Eng., Univ. of Kurdistan, Sanandaj, Iran (Abdollahpouri, A.) \|\| Modern Banking Dept., Negin Software Dev. Co. (TOSAN), Tehran, Iran (Abdollahpouri, H.)
Abstract	Number of services that banks offer customers, has been increased significantly in recent years. There is a variety of services available through the different channels like internet bank, mobile bank, telephone bank, ATM and so forth. The need to personalize these services for customers is felt more than before since people are busier than ever and have a limited time to do their daily tasks. In addition, in some banking channels like telephone bank, giving a service in a high speed manner is very important since the user has to listen to a variety of menu items and select one he want. Having a personal bank for each customer is an interesting issue for customers that can save their time doing banking tasks and it will improve customer satisfaction. In this paper, we present an architecture for implementation of this service personalization in a multi-channel environment with the help of a powerful approach called memory-based collaborative filtering. It has the ability to process the information coming from different banking channels according to the recent services that a customer has previously used and services that other customers have used most frequently.
Starting Page	208
Ending Page	213
File Size	358355
Page Count	6
File Format	PDF
e-ISBN	9781467364904
DOI	10.1109/IKT.2013.6620066
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-28
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memory-based collaborative filtering Personalization Collaborative filtering Collaboration Banking Mobile communication Multichannel environment Information filtering History Recommender systems Recommender system
Content Type	Text
Resource Type	Article

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