dc.contributor.author | Jabakji, Ammar | |
dc.contributor.author | Daǧ, Hasan | |
dc.date.accessioned | 2019-06-27T08:01:54Z | |
dc.date.available | 2019-06-27T08:01:54Z | |
dc.date.issued | 2016 | |
dc.identifier.isbn | 9781467390057 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12469/500 | |
dc.identifier.uri | https://doi.org/10.1109/BigData.2016.7840789 | |
dc.description.abstract | Recommendation systems play a critical role in the Information Science application domain especially in e-commerce ecosystems. In almost all recommender systems statistical methods and machine learning techniques are used to recommend items to the users. Although the user-based collaborative filtering approaches have been applied successfully in many different domains some serious challenges remain especially in regards to large e-commerce sites for recommender systems need to manage millions of users and millions of catalog products. In particular the need to scan a vast number of potential neighbors makes it very hard to compute predictions. Many researchers have been trying to come up with solutions like using neighborhood-based collaborative filtering algorithms model-based collaborative filtering algorithms and text mining algorithms. Others have proposed new methods or have built various architectures/frameworks. In this paper we proposed a new data model based on users'preferences to improve item-based recommendation accuracy by using the Apache Mahout library. We also present details of the implementation of this model on a dataset taken from Amazon. Our experimental results indicate that the proposed model can achieve appreciable improvements in terms of recommendation quality. | |
dc.language.iso | English | |
dc.publisher | IEEE | |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Recommendation Systems | |
dc.subject | Collaboration Filtering Mahout | |
dc.subject | Mean Absolute Error (MAE) | |
dc.title | Improving Item-Based Recommendation Accuracy with User's Preferences on Apache Mahout | |
dc.type | Proceedings Paper | |
dc.identifier.startpage | 1742 | |
dc.identifier.endpage | 1749 | |
dc.relation.journal | 2016 IEEE International Conference on Big Data (Big Data) | |
dc.identifier.wos | WOS:000399115001096 | |
dc.identifier.doi | 10.1109/BigData.2016.7840789 | |
dc.contributor.khasauthor | Daǧ, Hasan | |