On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative...
Main Authors: | Dimitris Spiliotopoulos, Dionisis Margaris, Costas Vassilakis |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-09-01
|
Series: | Information |
Subjects: | |
Online Access: | https://www.mdpi.com/2078-2489/13/9/428 |
Similar Items
-
On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets
by: Dionisis Margaris, et al.
Published: (2022-06-01) -
Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering
by: Dionisis Margaris, et al.
Published: (2021-09-01) -
Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets
by: Dionisis Margaris, et al.
Published: (2023-03-01) -
An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings
by: Dionisis Margaris, et al.
Published: (2020-07-01) -
An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy
by: Dionisis Margaris, et al.
Published: (2020-01-01)