Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6874343 | Journal of Computational Science | 2018 | 13 Pages |
Abstract
Providing a reliability value to each prediction and recommendation is very important in current recommender systems: Users should know which recommendations are reliable and which ones are risky. Despite its growing importance, research into collaborative filtering reliability has rarely been developed in the model-based area. This paper explains a matrix factorization-based architecture and method that provides a reliability value to each prediction/recommendation. The reliability values obtained have been put to the test, and, when applied, they show improvements in prediction and recommendation quality in different recommender systems; additionally, they provide a range of values that are understandable to users.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
B. Zhu, F. Ortega, J. Bobadilla, A. GutiƩrrez,