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Implementation of Item and Content based Collaborative Filtering Techniques based on Ratings Average for Recommender Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 65 - Number 24 |
| Year of Publication: 2013 |
| Authors: Rohini Nair, Kavita Kelkar |
10.5120/11229-6409
|
Rohini Nair, Kavita Kelkar . Implementation of Item and Content based Collaborative Filtering Techniques based on Ratings Average for Recommender Systems. International Journal of Computer Applications. 65, 24 ( March 2013), 1-5. DOI=10.5120/11229-6409
Abstract
Internet provides lots of information which is useful for recommender systems. Recommender systems are mostly used where web information is available in abundance in applications like book ecommerce. There exists many approaches to achieve recommendations like basic techniques of collaborative filtering and content based approach. These approaches can be done individually or combined depending on the type of recommendations needed by individuals. Recommender systems suggests items to purchase according to the users interest. Almost all applications in e commerce are working on the concept of recommendation system. It predicts recommendations while searching through large amount of information so that users can have the easiest access to the needed that best meet their needs and preferences. Literature survey is done on recommender systems shows that a lot of work is being carried out in this area and the project proposes a combination of various techniques of recommendation systems. All are based on the basic techniques include item based approach and content based which are the basic building blocks for a recommender systems. In this paper both algorithms are implemented and their respective results are presented and compared.
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