Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm
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 tha...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6643888 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560326627819520 |
|---|---|
| author | Hui Ning Qian Li |
| author_facet | Hui Ning Qian Li |
| author_sort | Hui Ning |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8a73a602607b4b06bb07d6ea443355c2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8a73a602607b4b06bb07d6ea443355c22025-02-03T01:27:57ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66438886643888Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering AlgorithmHui Ning0Qian Li1The College of Humanities and Economic Management, Xi’an Traffic Engineering Institute, Xi’an 710300, Shaanxi, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, Shaanxi, ChinaCollaborative 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.http://dx.doi.org/10.1155/2020/6643888 |
| spellingShingle | Hui Ning Qian Li Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm Complexity |
| title | Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm |
| title_full | Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm |
| title_fullStr | Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm |
| title_full_unstemmed | Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm |
| title_short | Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm |
| title_sort | personalized music recommendation simulation based on improved collaborative filtering algorithm |
| url | http://dx.doi.org/10.1155/2020/6643888 |
| work_keys_str_mv | AT huining personalizedmusicrecommendationsimulationbasedonimprovedcollaborativefilteringalgorithm AT qianli personalizedmusicrecommendationsimulationbasedonimprovedcollaborativefilteringalgorithm |