Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/293
Title: | A dynamic multi-level collaborative filtering method for improved recommendations |
Authors: | Polatidis, Nikolaos Georgiadis, Christos K. |
Type: | Article |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | cs.IR Collaborative filtering Dynamic multi-level Recommender systems |
Issue Date: | 2017 |
Publisher: | Elsevier |
Source: | Computer Standards & Interfaces |
Volume: | 51 |
First Page: | 14 |
Last Page: | 21 |
Abstract: | One of the most used approaches for providing recommendations in various online environments such as e-commerce is collaborative filtering. Although, this is a simple method for recommending items or services, accuracy and quality problems still exist. Thus, we propose a dynamic multi-level collaborative filtering method that improves the quality of the recommendations. The proposed method is based on positive and negative adjustments and can be used in different domains that utilize collaborative filtering to increase the quality of the user experience. Furthermore, the effectiveness of the proposed method is shown by providing an extensive experimental evaluation based on three real datasets and by comparisons to alternative methods. |
URI: | https://doi.org/10.1016/j.csi.2016.10.014 https://ruomo.lib.uom.gr/handle/7000/293 |
ISSN: | 09205489 |
Other Identifiers: | 10.1016/j.csi.2016.10.014 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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Georgiadis_2017_A dynamic.pdf | 982,82 kB | Adobe PDF | View/Open |
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