{"type":"ScholarlyArticle","title":"Handling Uncertainty in Recommender Systems under the Belief Function Theory","authors":[{"name":"Raoua Abdelkhalek","slug":"raoua-abdelkhalek","given":"Raoua","family":"Abdelkhalek","affiliation":"Institut Supérieur de Gestion de Tunis Université de Tunis , Tunis , Tunisia"}],"abstract":"Dealing with uncertainty is an important challenge in real world applications including Recommender Systems (RSs). Different kinds of uncertainty can be pervaded at any level throughout the recommendation process, which follows to inaccurate results.  The main goal of this research work is to consider RSs under an uncertain framework. We seek for an improvement over the traditional recommendation approaches in order to handle such uncertainty under the belief function theory.","doi":"10.24963/ijcai.2018/824","url":"https://ijcai.org/proceedings/2018/824","pdf":"https://ijcai.org/proceedings/2018/0824.pdf","markdown":"https://ijcai.org/proceedings/2018/824.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/824","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":824,"pages":{"first":5761,"last":5762},"keywords":["Uncertainty in AI: Uncertainty in AI","Machine Learning: Classification","Uncertainty in AI: Uncertainty Representations","Machine Learning: Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Abdelkhalek et al., 2016] Raoua Abdelkhalek, Imen Boukhris, and Zied Elouedi. Evidential item-based collaborative filtering. In International Conference on Knowledge Science, Engineering and Management, pages 628-639. Springer, 2016.","title":"Raoua Abdelkhalek, Imen Boukhris, and Zied Elouedi. Evidential item-based collaborative filtering","authors":"Abdelkhalek","year":"2016","venue":"International Conference on Knowledge Science, Engineering and Management","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Abdelkhalek et al., 2017a] Raoua Abdelkhalek, Imen Boukhris, and Zied Elouedi. Assessing items reliability for collaborative filtering within the belief function frame- work. In International Conference on Digital Economy, pages 208-217. Springer, 2017.","title":"Assessing items reliability for collaborative filtering within the belief function framework","authors":"Abdelkhalek","year":"2017","venue":"International Conference on Digital Economy","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Abdelkhalek et al., 2017b] Raoua Abdelkhalek, Imen Boukhris, and Zied Elouedi. A clustering approach for collaborative filtering under the belief function framework. In European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, pages 169-178. Springer, 2017.","title":"A clustering approach for collaborative filtering under the belief function framework","authors":"Abdelkhalek","year":"2017","venue":"European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Abdelkhalek et al., 2017c] Raoua Abdelkhalek, Imen Boukhris, and Zied Elouedi. A new user-based col- laborative filtering under the belief function theory. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 315-324. Springer, 2017.","title":"Zied Elouedi. A new user-based collaborative filtering under the belief function theory","authors":"Abdelkhalek","year":"2017","venue":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dempster, 1968] Arthur P Dempster. A generalization of bayesian inference. In Journal of the Royal Statistical So- ciety, Series B, volume 30, pages 205-247. 1968.","title":"A generalization of bayesian inference","authors":"P Arthur, Dempster","year":"1968","venue":"Journal of the Royal Statistical Society, Series B","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Ricci et al., 2015] Francesco Ricci, Lior Rokach, and Bracha Shapira. Recommender systems: introduction and challenges. In Recommender systems handbook, pages 1- 34. Springer, 2015.","title":"Recommender systems: introduction and challenges","authors":"Francesco Ricci, Lior Rokach, Bracha Shapira","year":"2015","venue":"Recommender systems handbook","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Shafer, 1976] Glenn Shafer. A mathematical theory of evi- dence, volume 42. Princeton university press, 1976.","title":"A mathematical theory of evidence","authors":"Glenn Shafer","year":"1976","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Su and Khoshgoftaar, 2009] Xiaoyuan Su and Taghi M Khoshgoftaar. A survey of collaborative filtering tech- niques. Advances in artificial intelligence, 2009:4, 2009.","title":"A survey of collaborative filtering techniques","authors":"Xiaoyuan Su, M Taghi, Khoshgoftaar","year":"2009","venue":"Advances in artificial intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}