.png){kind=icon}
返回
Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services
.png){kind=icon}
OA
AI
DOI：10.3390/bdcc7020106.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
adaptive K-nearest neighbor
collaborative filtering
recommendation system
user cognition
期刊
B
IF:
4.4
论文数:
1.3K
被引数:
2.4K
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏A hybrid recommender system for recommending relevant movies using an expert system使用专家系统推荐相关电影的混合推荐系统
PREAI
分享收藏
查看更多内容