A music recommendation algorithm based on clustering and latent factor model
The collaborative filtering recommendation algorithm is a technique for predicting items that a user may be interested in based on user history preferences. In the recommendation process of music data, it is often difficult to score music and the display score data for music is less, resulting in da...
Main Authors: | Jin Yingjie, Han Chunyan |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2020-01-01
|
Series: | MATEC Web of Conferences |
Subjects: | |
Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_03009.pdf |
Similar Items
-
Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback
by: Iwao Tanuma, et al.
Published: (2022-01-01) -
Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
by: Abebe Tegene, et al.
Published: (2023-01-01) -
Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
by: Yuyu Yuan, et al.
Published: (2019-10-01) -
Collaborative Filtering Recommendation Based on All-Weighted Matrix Factorization and Fast Optimization
by: Hongmei Li, et al.
Published: (2018-01-01) -
Ensemble divide and conquer approach to solve the rating scores’ deviation in recommendation system
by: Al-Hadi, Ismail Ahmed Al-Qasem, et al.
Published: (2016)