Enhancing Item-Based Collaborative Filtering with Item Correlations for Music Recommendation System
M. Sunitha1, T. Adilakshmi2, Mir Zahed Ali3

1M. Sunitha*, Assistant Professor, Department of CSE, Hyderabad, (Telangana), India.
Dr. T. Adilakshmi, Head & Professor, Department of CSE, Hyderabad, (Telangana), India.
3Mir Zahed Ali, M.Tech Student, CSE, Hyderabad, (Telangana), India. 

Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 1548-1553 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2016059120/2020©BEIESP | DOI: 10.35940/ijrte.A2016.059120
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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: Music recommendation systems are playing a vital role in suggesting music to the users from huge volumes of digital libraries available. Collaborative filtering (CF) is a one of the well known method used in recommendation systems. CF is either user centric or item centric. The former is known as user-based CF and later is known as item-based CF. This paper proposes an enhancement to item-based collaborative filtering method by considering correlation among items. Lift and Pearson Correlation coefficient are used to find the correlation among items. Song correlation matrix is constructed by using correlation measures. Proposed method is evaluated on the benchmark dataset and results obtained are compared with basic item-based CF. 
Keywords: Music recommendation system, Collaborative filtering, User-based CF, item-based CF, Lift, Pearson Correlation coefficient, Song correlation matrix.
Scope of the Article: Collaborative applications