A User-Item Predictive Model for Collaborative Filtering Recommendation

机译：协同过滤推荐的用户项目预测模型

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Collaborative Filtering recommender systems, one of the most representative systems for personalized recommendations in E-commerce, enable users to find the useful information easily. But traditional CF suffers from some weaknesses: scalability and real-time performance. To address these issues, we present a novel model-based CF approach to provide efficient recommendations. In addition, we propose a new method of building a model with dynamic updates, when users present explicit feedback. The experimental evaluation on MovieLens datasets shows that our method offers reasonable prediction quality as good as the best of user-based Pearson correlation coefficient algorithm.

机译：协同过滤推荐器系统是电子商务中个性化推荐的最具代表性的系统之一，使用户可以轻松找到有用的信息。但是传统的CF有一些缺点：可伸缩性和实时性能。为了解决这些问题，我们提出了一种新颖的基于模型的CF方法，以提供有效的建议。此外，当用户提出明确的反馈时，我们提出了一种使用动态更新构建模型的新方法。在MovieLens数据集上的实验评估表明，我们的方法提供了合理的预测质量，与基于用户的最佳Pearson相关系数算法一样好。

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来源
《International Conference on User Modeling(UM 2007); 20070625-29; Corfu(GR)》|2007年|P.324-328|共5页
会议地点 Corfu(GR)
作者
Heung-Nam Kim; Ae-Ttie Ji; Cheol Yeon; Geun-Sik Jo;
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Intelligent E-Commerce Systems Lab., Dept. of Information Engineering, Inha University;

School of Information Engineering, Inha University, Incheon, Korea;

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原文格式 PDF
正文语种 eng
中图分类计算机的应用;
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专利

1. Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering [J] . Anand Shanker Tewari Procedia Computer Science . 2020,第5期

机译：通过熔断内容和基于用户项的协作过滤生成项目建议
2. A User-Item Collaborative Filtering System to Predict Online Learning Outcome [J] . Dina Fitria Murad, Rosilah Hassan, Wahiza Wahi, Advances in Science, Technology and Engineering Systems . 2020,第5期

机译：用户项目协作过滤系统，以预测在线学习结果
3. Discovery of user-item subgroups via genetic algorithm for effective prediction of ratings in collaborative filtering [J] . Laishram Ayangleima, Padmanabhan Vineet Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies . 2019,第11期

机译：通过遗传算法发现用户项目子组，以便在协同过滤中有效预测额定值
4. A User-Item Predictive Model for Collaborative Filtering Recommendation [C] . Heung-Nam Kim, Ae-Ttie Ji, Cheol Yeon, International Conference on User Modeling . 2007

机译：用于协同过滤推荐的用户项目预测模型
5. Group modeling, recommendation and evaluation in collaborative filtering group-based recommender systems [D] . Najjar, Nadia Abusaymeh 2015

机译：基于协作过滤基于组的推荐器系统中的组建模，推荐和评估
6. Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering [O] . Tae-Yeun Kim, Hoon Ko, Sung-Hwan Kim, 2021

机译：基于情感信息和协作筛选的推荐系统建模
7. CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering [O] . Quangui Zhang, Longbing Cao, Chengzhang Zhu, 2018

机译：COMPECEDCF：在深度协同过滤的建议书中学习显式和隐式用户项目耦合

A User-Item Predictive Model for Collaborative Filtering Recommendation

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