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

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