Comparison of User-based Collaborative Filtering model for Music Recommendation System with various proximity measures
M. Sunitha1, T. Adilakshmi2
1M. Sunitha, Department of Computer Science and Engineering, Vasavi College of Engineering Ibrahimbagh, Hyderabad, TG, India.
2Dr. T. Adilakshmi, Department of Computer Science and Engineering, Vasavi College of Engineering Ibrahimbagh, Hyderabad, TG, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 24 May 2019 | PP: 15-21 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F22030486S219/19©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: The predominant growth of Internet, Smart phones and online music service providers led to enormous amount of digital music. It becomes immensely important for service providers to search, map and provide users with the relevant chunk of music according to their preferences and taste. Recommendation systems (RS) are built to help users in finding relevant information. RS plays an important role in recommending items such as music, books, movies, restaurants etc. This paper presents a user-based collaborative filtering model for music recommendation. Various proximity measures are used in model building. User-based collaborative filtering model is implemented on standard dataset obtained from Last.fm and the results are compared with most popular, nearest neighbor and clustering methods.
Keywords: Recommendation System, Collaborative Filtering, Proximity Measures, Nearest Neighbor, Clustering.
Scope of the Article: Computer Science and Its Applications