Affiliation:
1. Department of Artificial Intelligence, FPT University, Da Nang 550000, Vietnam
2. Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Abstract
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.
Subject
Artificial Intelligence,Computer Science Applications,Information Systems,Management Information Systems
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