Social Influence-Based Similarity Measures for User-User Collaborative Filtering Applied to Music Recommendation

2019 ◽  
pp. 267-274 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Javier Pérez-Marcos ◽  
Ana B. Gil González ◽  
Vivian López Batista ◽  
María N. Moreno-García
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Similarity Measures ◽  
Music Recommendation

scholarly journals Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Meilin Lu ◽  
Fangfang Deng
Keyword(s):  
Collaborative Filtering ◽  
Expectation Maximization Algorithm ◽  
Similarity Measures ◽  
Tensor Decomposition ◽  
User Preferences ◽  
Music Recommendation ◽  
Actual Case ◽  
Ethnic Music ◽  
Ontology Modeling ◽  
National Music

Personalized music recommendations can accurately push the music of interest from a massive song library based on user information when the user’s listening needs are blurred. To this end, this paper proposes a method of national music recommendation based on ontology modeling and context awareness to explore the use of music resources to portray user preferences better. First, the expectation-maximization algorithm is used to cluster users and ethnic music scores, and similar users and music are divided into clusters. The similarity of objects in the same cluster is higher, and the similarity of objects in different clusters is lower. Second, we designed a multilayer collaborative filtering ethnic music recommendation model based on ontology modeling and tensor decomposition. This model uses ontology to construct a user knowledge model and integrates similarity measures in multiple situations. The actual case test and user feedback analysis show that the designed personalized national music model has good application and promotion effects.

Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

2020 ◽  
Vol 8 (4) ◽  
pp. 367
Author(s):  
Muhammad Arief Budiman ◽  
Gst. Ayu Vida Mastrika Giri
Keyword(s):  
Collaborative Filtering ◽  
Mobile Phones ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Music Recommendation ◽  
K Nearest Neighbor ◽  
Filtering Method ◽  
K Nearest Neighbor Algorithm ◽  
Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

Music Recommendation Based on Multidimensional Description and Similarity Measures

2013 ◽  
Vol 127 (1-4) ◽  
pp. 325-340 ◽  
Author(s):  
Bozena Kostek ◽  
Andrzej Kaczmarek
Keyword(s):  
Similarity Measures ◽  
Music Recommendation

Exploring Set-Inspired Similarity Measures for Collaborative Filtering Recommendation

2021 ◽  
Author(s):  
Quang-Hung Le ◽  
Thi-Xinh Le
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measures

scholarly journals Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hui Ning ◽  
Qian Li
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Contextual Information ◽  
Rapid Development ◽  
Theoretical Research ◽  
Context Information ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Points Of Interest ◽  
Popularity Prediction

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP (Media Recommendation Algorithm based on Popularity Prediction). The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated.

scholarly journals AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 427 ◽  
Author(s):  
Zahir ◽  
Yuan ◽  
Moniz
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Computational Cost ◽  
Recommendation Systems ◽  
Similarity Measures ◽  
Extraction Methods ◽  
Common Interest ◽  
Trust Relationship ◽  
Public Data ◽  
Implicit Trust

Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.

Sign in / Sign up

Export Citation Format

Share Document