Experimental interpretation of adequate weight-metric combination for dynamic user-based collaborative filtering.

ABSTRACT: Recommender systems include a broad scope of applications and are associated with subjective preferences, indicating variations in recommendations. As a field of data science and machine learning, recommender systems require both statistical perspectives and sufficient performance monitoring. In this paper, we propose diversified similarity measurements by observing recommendation performance using generic metrics. Considering user-based collaborative filtering, the probability of an item being preferred by any user is measured. Having examined the best neighbor counts, we verified the test item bias phenomenon for similarity equations. Because of the statistical parameters used for computing in a global scope, there is implicit information in the literature, whether those parameter

SUBMITTER: Okyay S 

PROVIDER: S-EPMC11566190 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature