ref http://nuoku.vip/users/2/articles/166
本paper 中的Eq13 关于m(\Theta)的描述有问题，应该改成Eq14加号后的部分。
填表计算案例见笔记本手工笔记。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Evidential Item-Based Collaborative Filtering | |||||||||||
| 文献作者 | Raoua Abdelkhalek | ||||||||||
| 文献发表年限 | 2016 | ||||||||||
| 文献关键字 | |||||||||||
| Recommender systems · Collaborative filtering · Belief function theory · Uncertainty · Evidential K-Nearest Neighbors；Dempster–Shafer theory; DS证据理论； 证据合成规则 | |||||||||||
| 摘要描述 | |||||||||||
| Recommender Systems (RSs) in particular the collaborative filtering approaches have reached a high level of popularity. These approaches are designed for predicting the user’s future interests towards unrated items. However, the provided predictions should be taken with restraint because of the uncertainty pervading the real-world problems. Indeed, to not give consideration to such uncertainty may lead to unrep- resentative results which can deeply affect the predictions’ accuracy as well as the user’s confidence towards the RS. In order to tackle this issue, we propose in this paper a new evidential item-based collaborative fil- tering approach. In our approach, we involve the belief function theory tools as well as the Evidential K-Nearest Neighbors (EKNN) classifier to deal with the uncertain aspect of items’ recommendation ignored by the classical methods. The performance of our new recommendation app- roach is proved through a comparative evaluation with several traditional collaborative filtering recommenders. | |||||||||||
