Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/2231
Title: A similarity measure model based on the dissimilarity degree between users
Authors: Al-Safi, Jehan
Kaleli, Cihan
Keywords: Cold-start
Recommender system
Similarity measure
User dissimilarity
Issue Date: 2021
Publisher: Taylor & Francis Ltd
Abstract: This article offers a new users-similarity measure to improve recommender systems' accuracy even when a few ratings are available. This model is based on the degree of dissimilarity between common ratings. On real data sets, we conducted our experiments to evaluate the proposed model's accuracy. Experiments demonstrate the model's capacity to locate a suitable neighborhood, resolve the user's cold-start problem, and significantly improve recommendation accuracy. Thus, this research may be considered a supplement to previous studies on user cold-start and good neighbor selection difficulties in user-based collaborative filtering algorithms.
URI: http://doi.org/10.1080/02522667.2021.1967592
https://hdl.handle.net/20.500.13087/2231
ISSN: 0252-2667
2169-0103
Appears in Collections:Bilgisayar Mühendisliği Bölümü Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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