基于自适应KNN的扩展协同过滤推荐服务

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Luong Vuong Nguyen, Quoc-Trinh Vo, Tri-Hai Nguyen
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引用次数: 3

摘要

在当前的电子商务时代,用户被无数的产品淹没,很难找到相关的商品。推荐系统根据用户偏好生成建议,以避免信息过载。协同过滤是现代推荐系统中广泛使用的一种模型。尽管协作过滤很受欢迎,但它也有研究人员想要克服的局限性。在本文中,我们通过考虑用户认知的相似性,增强了推荐系统中基于K近邻(KNN)的协同过滤算法。这种增强旨在提高对用户进行分组的准确性,并为活动用户生成更相关的推荐。实验结果表明,在MAE、RMSE、MAP和NDCG指标方面,所提出的模型优于基准模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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