08-08-2014, 03:21 PM
Review Of Different Clustering Techniques Used In Recommender System
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Abstract:
This paper gives review about different clustering techniques used in recommender system along with their advantages and disadvantages. Nowadays, use of Internet for online shopping is increasing. Many sites provide platform for online shopping. The major task for these sites is to recommend proper items to the customers based on their purchase history, demographic information, similarity in customer, etc. Clustering is the basic and important method in understanding grouping a data set and identifying customers with similar interests, identifying items with similar attributes.
Introduction:
Recommendation systems have become an important research area and there has been much work done both in industry and academia on developing new approaches to recommender systems over the last decade [1]. There has been much work done both in the industry and academia on developing new approaches to recommender systems over the last decade. The interest in this area still remains high because it constitutes a problem-rich research area and because of the abundance of practical applications that help users to deal with information
overload and provide personalized recommendations, content, and services to them [5]. In general, every recommendation system follows a specific process in order to produce product recommendations [2].
BACK GROUND WORK RECOMMENDER SYSTEMS
Nowadays the use of internet for online shopping is increasing day-by-day. Online stores, online retailers are able to sell more products than physical store. But one biggest disadvantage is every time customer has to browse all the categories and sub-categories of products to find the products exactly they want. That is why Recommender Systems are very much needed in the e-commerce websites. It gives suitable personalized recommendation to every single customer based on the knowledge about the customer and products. It encourages customers to buy the products, they didn’t plan to buy. The more a customer uses a website and purchases items, the more the recommender system learns about the customer and the better the recommendations get. Recommender system is classified a
Conclusion
The several clustering techniques have been proposed that are used in recommender system which are challenges for research work. It is required to work on this research area to explore and provide new methods which reduce the challenges and provide recommendation in a wide range of applications. Thus, the current clustering techniques need improvement for present and future requirements of better recommendation qualities.