Enhancing recommendation systems performance using highly-effective similarity measures
Abstract In Recommendation Systems (RS) and Collaborative Filtering (CF), the similarity
measures have been the operating component upon which CF performance is essentially …
measures have been the operating component upon which CF performance is essentially …
Research on antecedents of residents' willingness to cooperate in urban regeneration projects: based on an extended theory of planned behavior (TPB) model
Residents' willingness to cooperate can contribute to the success of urban regeneration
projects worldwide. However, limited research has explored factors determining residents' …
projects worldwide. However, limited research has explored factors determining residents' …
Smoke detection method based on LBP and SVM from surveillance camera
Wildfire is a regular incident worldwide today. It destroys forests and also the living areas of
wild animals. So, to reduce the harmful effects of such disasters this paper describes a …
wild animals. So, to reduce the harmful effects of such disasters this paper describes a …
Combinations of jaccard with numerical measures for collaborative filtering enhancement: Current work and future proposal
Collaborative filtering (CF) is an important approach for recommendation system which is
widely used in a great number of aspects of our life, heavily in the online-based commercial …
widely used in a great number of aspects of our life, heavily in the online-based commercial …
Mitigating sparsity using Bhattacharyya Coefficient and items' categorical attributes: Improving the performance of collaborative filtering based recommendation …
Collaborative filtering has been the most popular and effective recommendation technique
to predict ratings using similar users or items. But in a sparse dataset, due to fewer co-rated …
to predict ratings using similar users or items. But in a sparse dataset, due to fewer co-rated …
Numerical Similarity Measures Versus Jaccard for Collaborative Filtering
Collaborative filtering (CF) is an important method for recommendation systems, which are
employed in many facets of our lives and are particularly prevalent in online-based …
employed in many facets of our lives and are particularly prevalent in online-based …
On the Impact of Jaccard Fusion with Numerical Measures for Collaborative Filtering Enhancement
Collaborative filtering (CF) is an important method for recommendation systems, which are
employed in many facets of our lives and are particularly prevalent in online-based …
employed in many facets of our lives and are particularly prevalent in online-based …
[PDF][PDF] Employing serendipity in recommender systems to overcome the popularity bias and long tail problem
SS TANDEL - gtusitecirculars.s3.amazonaws.com
Assisting users to aid in decision making while doing e-commerce purchases is a primary
task of a traditional Recommender System. But often there is a necessity to give equitable …
task of a traditional Recommender System. But often there is a necessity to give equitable …
[PDF][PDF] Enhancing Performance of Recommendation Systems through Intelligent Collaborative Filtering Techniques: Future Work Proposal
AA Amer - researchgate.net
This project introduces advanced AI techniques to promote collaborative filtering (CF)
performance. Using the K-nearest neighbor algorithm (KNN), a comprehensive CF …
performance. Using the K-nearest neighbor algorithm (KNN), a comprehensive CF …