Dynamic Music Recommender System Using Genetic Algorithm
Manjula Athani1, Neelam Pathak2, Asif Ullah Khan3
1TMrs. Manjula Athani, CSE, T.I.T, RGPV Bhopal, India.
2Prof. Neelam Pathak, IT, Dept, T.I.T Excellence, RGPV, Bhopal, India.
3Prof. Asif Ullah Khan, CSE, T.I.T, RGPV, Bhopal, India.
Manuscript received on March 27, 2014. | Revised Manuscript received on April 06, 2014. | Manuscript published on April 30, 2014. | PP: 230-232 | Volume-3, Issue-4, April 2014. | Retrieval Number: D2957043414/2013©BEIESP
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© The Authors. 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: Web-based systems are popular in many different areas, with the users they tend to deliver customized information by means of utilization of recommendation methods. The recommender system also has to recognize and provide items corresponding with user favorites. In this paper we presented a dynamic recommender system for music data. This system is able to identifying the n-number of users preferences and adaptively recommend music tracks according to user preferences. we are extracting unique feature of each music track. Then we are applying BLX-a crossover to a extracted features of each music track. User favorite and user profiles are included. Multiuser dynamic recommender system for n-user combines the two methodologies, the content based filtering technique and the interactive genetic algorithm by providing optimized solution every time and which is based on user’s preferences hence it give better result and better user system..
Keywords: Recommender system, Interactive genetic algorithm BLX-a crossover.