Two Models Based on Social Relations and SVD++ Method for Recommendation System
Dublin Core
Title
Two Models Based on Social Relations and SVD++ Method for Recommendation System
Subject
recommendation system
SVD
social relations
data sparsity
cold-start
Description
Recently, Recommender Systems (RSs) have attracted many researchers whose goal is to improve the performance of the prediction accuracy of recommendation systems by alleviating RSs drawbacks. The most common limitations are sparsity and the cold-start problem. This article proposes two models to mitigate the effects of these limitations. The proposed models exploit five sources of information: rating information, which involves two sources, namely explicit and implicit, which can be extracted via users’ ratings, and two types of social relations: explicit and implicit relations, the last source is confidence values that are included in the first model only. The whole sources are combined into the Singular Value Decomposition plus (SVD++) method. First, to extract implicit relations, each non-friend pair of users, the Multi-Steps Resource Allocation (MSRA) method is adopted to compute the probability of being friends. If the probability has accepted value which exceeds a threshold, an implicit relationship will be created. Second, the similarity of explicit and implicit social relationships for each pair of users is computed. Regarding the first model, a confidence value between each pair of users is computed by dividing the number of common items by the total number of items which have also rated by the first user of this pair. The confidence values are combined with the similarity values to produce the weight factor. Furthermore, the weight factor, explicit, and implicit feedback information are integrated into the SVD++ method to compute the missing prediction values. Additionally, three standard datasets are utilized in this study, namely Last.Fm, Ciao, and FilmTrust, to evaluate our models. The experimental results have revealed that the proposed models outperformed state-of-the-art approaches in terms of accuracy.
Creator
Al Sabaawi, Ali M. Ahmed
Karacan, Hacer
Yenice, Yusuf Erkan
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 01 (2021); pp. 70-87
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2021-01-12
Rights
Copyright (c) 2021 Ali M. Ahmed Al Sabaawi, Hacer KARACAN, Yusuf Erkan YENİCE
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Al Sabaawi, Ali M. Ahmed, Hacer Karacan and Yusuf Yenice Erkan, Two Models Based on Social Relations and SVD++ Method for Recommendation System, International Association of Online Engineering (IAOE), Vienna, Austria, 2021, accessed November 5, 2024, https://igi.indrastra.com/items/show/1844