Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services

In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is a widely used model in modern recommendation s...

Full description

Saved in:
Bibliographic Details
Published inBig data and cognitive computing Vol. 7; no. 2; p. 106
Main Authors Nguyen, Luong Vuong, Vo, Quoc-Trinh, Nguyen, Tri-Hai
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Accuracy
adaptive K-nearest neighbor
Algorithms
Cognition
Collaboration
collaborative filtering
Datasets
Electronic commerce
Filtration
Methods
Motion pictures
Ratings & rankings
recommendation system
Recommender systems
Semantics
Sparsity
user cognition
Online AccessGet full text

Cover

More Information
Summary:In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is a widely used model in modern recommendation systems. Despite its popularity, collaborative filtering has limitations that researchers aim to overcome. In this paper, we enhance the K-nearest neighbor (KNN)-based collaborative filtering algorithm for a recommendation system, by considering the similarity of user cognition. This enhancement aimed to improve the accuracy in grouping users and generating more relevant recommendations for the active user. The experimental results showed that the proposed model outperformed benchmark models, in terms of MAE, RMSE, MAP, and NDCG metrics.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc7020106