Category preferred canopy–K-means based collaborative filtering algorithm
It is the era of information explosion and overload. The recommender systems can help
people quickly get the expected information when facing the enormous data flood …
people quickly get the expected information when facing the enormous data flood …
User-Item Matrix Reduction Technique for Personalized Recommender Systems
KJ Kim, HC Ahn - Journal of Information Technology Applications …, 2009 - koreascience.kr
Collaborative filtering (CF) has been a very successful approach for building recommender
system, but its widespread use has exposed to some well-known problems including …
system, but its widespread use has exposed to some well-known problems including …
Hybrid collaborative filtering model for improved recommendation
H Ji, J Li, C Ren, M He - Proceedings of 2013 IEEE …, 2013 - ieeexplore.ieee.org
Collaborative filtering (CF) based recommendation system, which can automatically predict
unknown preference of a user to certain products and then generate meaningful …
unknown preference of a user to certain products and then generate meaningful …
Collaborative filtering based on Gaussian mixture model and improved Jaccard similarity
H Yan, Y Tang - IEEE Access, 2019 - ieeexplore.ieee.org
The recommender systems play an important role in our lives, since it can quickly help users
find what they are interested in. Collaborative filtering has become one of the most widely …
find what they are interested in. Collaborative filtering has become one of the most widely …
An efficient approach for improving the predictive accuracy of multi-criteria recommender system
Recommender Systems are useful information filtering tools that have reduced information
overload over the web. Collaborative filtering (CF) is one of the extensively used …
overload over the web. Collaborative filtering (CF) is one of the extensively used …
Collaborative filtering and kNN based recommendation to overcome cold start and sparsity issues: A comparative analysis
Collaborative Filtering (CF) has intrigued several researchers whose goal is to enhance
Recommender System's performance by mitigating their drawbacks. CF's common idea is to …
Recommender System's performance by mitigating their drawbacks. CF's common idea is to …
[PDF][PDF] Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space
J Huang, Z Jia, P Zuo - Mathematical Modelling and Control, 2023 - aimspress.com
* Correspondence: Email: jjjzzz0@ 163. com. Abstract: Collaborative filtering (CF) algorithm
is one of the most widely used recommendation algorithms in recommender systems …
is one of the most widely used recommendation algorithms in recommender systems …
An item-based collaborative filtering using dimensionality reduction techniques on mahout framework
S Girase, D Mukhopadhyay - arXiv preprint arXiv:1503.06562, 2015 - arxiv.org
Collaborative Filtering is the most widely used prediction technique in Recommendation
System. Most of the current CF recommender systems maintains single criteria user rating in …
System. Most of the current CF recommender systems maintains single criteria user rating in …
Gauss-core extension dependent prediction algorithm for collaborative filtering recommendation
LB Xu, XS Li, Y Guo - Cluster Computing, 2019 - Springer
Most current researches on collaborative filtering recommendation generally improve the
performance of traditional algorithm within its framework. Different from these studies, the …
performance of traditional algorithm within its framework. Different from these studies, the …
Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item
The item-based collaborative filtering technique recommends an item to the user from the
rating of k-nearest items. Generally, a random value of k is considered to find nearest …
rating of k-nearest items. Generally, a random value of k is considered to find nearest …