Using Self-Organizing Maps for Recommender Systems
Niharika Purbey1, Khyati Pawde2, Shreya Gangan3, Ruhina Karani4
1Niharika Purbey, Computer Engineering,, Dwarkadas J Sanghvi College of Engineering, Mumbai, India.
2Khyati Pawde, Computer Engineering,, Dwarkadas J Sanghvi College of Engineering, Mumbai, India.
3Shreya Gangan, Computer Engineering,, Dwarkadas J Sanghvi College of Engineering, Mumbai, India.
4Ruhina Karani, Computer Engineering,, Dwarkadas J Sanghvi College of Engineering, Mumbai, India.
Manuscript received on November 03, 2014. | Revised Manuscript received on November 28, 2014. | Manuscript published on November 05, 2014. | PP: 47-50 | Volume-4 Issue-5, November 2014. | Retrieval Number: E2419114514/2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we present an approach called Self-Organizing Map and its application in recommendation systems. Self-Organizing map is a popular unsupervised artificial neural network algorithm. We discuss the SOM algorithm in detail and evaluate its performance. The SOM technique has various advantages over general mining algorithms and hence we choose to discuss this technique. Traditionally, with recommendation systems, collaborative filtering or hybrid systems are used. However, if these techniques are used with artificial neural networks like SOM, the system becomes more efficient.
Keywords: Self-Organizing Map (SOM), Recommender Systems, Neural Network, Feature Map, Unsupervised Learning.