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Journal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.6, No. 11)

Publication Date:

Authors : ; ;

Page : 145-150

Keywords : Recommendation system; k-mean clustering; collaborative filtering;

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With the increase in demand of items amongst customer enhances the growth in information technology and ecommerce websites. This demand is increased due to the availability of web services Personalized preferences and decision making are generated in an application called Recommendation system using an information filtering technique. Web related services and applications through which searching and selecting becomes easy, the related use which are in demand are selecting books and newspaper, best hotel and its location, ticket bookings like movies, flights, buses, trains etc. Relevant features and related items are the characteristic on which this technique works. Suggestion of items, according to user preferences are most important, so suggestion according to similarities provides suitable recommendation. The working of recommendation system for administration have been researched in recent years. Network resources, clients, and administration are all connected and quickly developed. This method of recommendation system works as suggestion, customization, learning, administration and this all provides user for the items suggestion and decision making. In this paper , variety of algorithms like k-mean clustering, collaborative filtering are used for the information suggestion

Last modified: 2017-11-08 21:21:00