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Collaborative Filtering Recommendation of Online Learning Resources for E-commerce Logistics Talent Training
The rapid development of online teaching has led to a surge in the number of online learning resources, but students are also prone to the problem of...
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Tourist Attraction Recommendation System Based on Django and Collaborative Filtering
Recently, with the development of social and economic levels and people's pursuit of quality of life, tourism has become the first choice for more...
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Wasserstein GAN-based architecture to generate collaborative filtering synthetic datasets
Currently, generative applications are resha** different fields, such as art, computer vision, speech processing, and natural language. The...
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Movie Recommendation Using Content-Based and Collaborative Filtering Approach
A recommender system is one that tries to anticipate or filter preferences based on the user’s selections. Films, music, journalism, publications,...
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UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering
In recent years, prototypes have gained traction as an interpretability concept in the Computer Vision Domain, and have also been explored in...
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Collaborative Filtering Using Matrix Factorization, Singular Value Decomposition, and Co-Clustering
Chapter 4 explored collaborative filtering and using the KNN method. A few more important methods are covered in...
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User Similarity Computation Strategy for Collaborative Filtering Using Word Sense Disambiguation Technique
Collaborative filtering is a sophisticated recommendation system strategy that efficiently manipulates recommendations corresponding to preferences...
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Survey on Collaborative Filtering Technique for Recommender System Using Deep Learning
Automated systems like Recommenders are developed to recommend item suggestions to consumers abiding a variety of conditions. They predict ratings of...
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Collaborative Filtering Based on Non-Negative Matrix Factorization for Programming Problem Recommendation
This paper explores the use of Non-Negative Factorization for adapting collaborative filtering to programming exercises. Traditional collaborative...
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Sparse Linear Capsules for Matrix Factorization-Based Collaborative Filtering
Collaborative filtering (CF) plays a key role in recommender systems, which consists of two basic disciplines: neighborhood methods and latent factor...
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A probabilistic linguistic and dual trust network-based user collaborative filtering model
Recommendation models for network information that are generally based on user ratings fail to utilize user online behaviours such as reviews and...
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Adaptive Exercise Recommendation Based on Cognitive Level and Collaborative Filtering
Adaptive learning is inseparable from adaptive testing, and adaptive testing needs to rely on students cognitive level for personalized...
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Collaborative Filtering Based on Probabilistic Rough Set C-Means Clustering
Collaborative filtering (CF) is a technique for realizing recommender systems found in e-commerce sites and video streaming sites. Appropriate...
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Design and Implementation of College Student Volunteer Service Platform Based on Collaborative Filtering Algorithm
In the past decade, voluntary service has mushroomed vigorously. With the promotion of voluntary service and the progress of the Internet, massive...
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DeepLSGR: Neural collaborative filtering for recommendation systems in smart community
In the field of Data science and online world, Recommendation Systems (RS) play an important role among the various e-commerce applications. Data...
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Collaborative filtering algorithm with social information and dynamic time windows
With the rapid development of social networks, the problem of information overload is increasingly serious. The recommendation system can deal with...
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Co-clustering neighborhood—based collaborative filtering framework using formal concept analysis
In recent times information on the web is growing exponentially and reaching to the point where humans can no longer deal with it manually or with...
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Feature-blind fairness in collaborative filtering recommender systems
Recommender systems were originally proposed for suggesting potentially relevant items to users, with the unique objective of providing accurate...
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Embarrassingly shallow auto-encoders for dynamic collaborative filtering
Recent work has shown that despite their simplicity, item-based models optimised through ridge regression can attain highly competitive results on...
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Combining Autoencoder with Adaptive Differential Privacy for Federated Collaborative Filtering
Recommender systems provide users personalized services by collecting and analyzing interaction data, undermining user privacy to a certain extent....