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Incremental Associative Memory Model Algorithm for Highly Scalable Recommender Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 68 - Number 21 |
| Year of Publication: 2013 |
| Authors: Neha Agarwal, Prashant |
10.5120/11705-7317
|
Neha Agarwal, Prashant . Incremental Associative Memory Model Algorithm for Highly Scalable Recommender Systems. International Journal of Computer Applications. 68, 21 ( April 2013), 34-37. DOI=10.5120/11705-7317
Abstract
Recommender systems are smart and intelligent systems that often seem to know users more than users know themselves. Recommender system helps customers by recommending products they will probably like or purchase based on their purchasing, searching, browsing history and also the other similar customer's history. Their aim is to provide efficient personalized solution in E-commerce domain that would benefit both buyer and seller. In this paper, authors proposed a neural network based approach called Associative Memory Model (AMM) to recommend items to users and also explain Incremental AMM for dynamic dataset. Experiments are carried out to observe the performance of the proposed algorithm and compare results with the existing traditional collaborative filtering algorithm . The property of AMM is that they are able to solve the pattern completion problem. This property can be used to build an efficient recommender system for E-commerce website that can produce more accurate and quick results than the others.
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