E-FCM Algorithms for Collaborative Tagging System

Banda, Latha; Bharadwaj, Kamal Kant

doi:10.1007/978-3-319-11933-5_11

Cited by 1 publication

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“…Choosing an appropriate recommendation algorithm is the core and key to the successful application of personalized recommendation system, and the performance of recommendation algorithm also has a direct impact on the recommendation quality. Currently, popular recommendation methods mainly include association rule recommendation, content-based recommendation [3], collaborative filtering recommendation, and hybrid recommendation technology [4,5]. Among them, collaborative filtering recommendation is the most widely used and successful technique in the field of recommendation, and it is also the most hot spot in the research of recommendation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Hui¹,

2020

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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.

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Section: Introductionmentioning

confidence: 99%

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Hui¹,

2020

Complexity

View full text Add to dashboard Cite

show abstract

E-FCM Algorithms for Collaborative Tagging System

Cited by 1 publication

References 11 publications

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

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