A Comparative Study of Music Recommendation Systems

2018 ◽  
Author(s):  
Ashish Patel ◽  
Rajesh Wadhvani
Keyword(s):  
Comparative Study ◽  
Recommendation Systems ◽  
Music Recommendation

Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

2011 ◽  
pp. 203-221 ◽  
Author(s):  
Zehra Cataltepe ◽  
Berna Altinel
Keyword(s):  
Demographic Data ◽  
Recommendation Systems ◽  
Personal Data ◽  
Music Recommendation ◽  
Music Preferences ◽  
User Models ◽  
Online Music ◽  
Important Field ◽  
Case Based

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.

Knowledge Management Techniques in Emotion-Based Music Recommendation Systems

2021 ◽  
pp. 415-423
Author(s):  
Catherine Marinagi ◽  
Paris Ntsounos ◽  
John Darryl Pelingo ◽  
Christos Skourlas ◽  
Anastasios Tsolakidis
Keyword(s):  
Knowledge Management ◽  
Recommendation Systems ◽  
Music Recommendation ◽  
Management Techniques

scholarly journals Recommendation Systems as Technologies of the Self: Algorithmic Control and the Formation of Music Taste

2017 ◽  
Vol 35 (2) ◽  
pp. 3-24 ◽  
Author(s):  
Nedim Karakayali ◽  
Burc Kostem ◽  
Idil Galip
Keyword(s):  
Soft Power ◽  
Recommendation Systems ◽  
The Self ◽  
Music Recommendation ◽  
Web Technologies ◽  
Technologies Of The Self ◽  
Cultural Medium ◽  
Care Practices ◽  
Creative Self ◽  
To Receive

The article brings to light the use of recommender systems as technologies of the self, complementing the observations in current literature regarding their employment as technologies of ‘soft’ power. User practices on the music recommendation website last.fm reveal that many users do not only utilize the website to receive guidance about music products but also to examine and transform an aspect of their self, i.e. their ‘music taste’. The capacity of assisting users in self-cultivation practices, however, is not unique to last.fm but stems from certain properties shared by all recommendation systems. Furthermore, unlike other oft-studied digital/web technologies of the self which facilitate ‘self-publishing’ vis-à-vis virtual companions in social media, recommender algorithms themselves can act as ‘intimate experts’, accompanying users in their self-care practices. Thus, recommendation systems can facilitate both algorithmic control and creative self-transformation, which calls for a theorization of this new cultural medium as a space of tension.

scholarly journals Enhancing Item-Based Collaborative Filtering with Item Correlations for Music Recommendation System

2020 ◽  
Vol 9 (1) ◽  
pp. 1548-1553
Keyword(s):  
Collaborative Filtering ◽  
Digital Libraries ◽  
Correlation Matrix ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Vital Role ◽  
Music Recommendation ◽  
Filtering Method ◽  
Music Recommendation System

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

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

Comparative Study on Approaches of Recommendation Systems

2020 ◽  
pp. 753-764
Author(s):  
Khalid Al Fararni ◽  
Badraddine Aghoutane ◽  
Jamal Riffi ◽  
Abdelouahed Sabri ◽  
Ali Yahyaouy
Keyword(s):  
Comparative Study ◽  
Recommendation Systems
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