Recommendation System Algorithms on Location-Based Social Networks: Comparative Study

被引：9
作者：
Al-Nafjan, Abeer [1 ]
Alrashoudi, Norah [2 ]
Alrasheed, Hend [2 ]
机构：
[1] Imam Muhammad Bin Saud Univ, Coll Comp & Informat Sci, Dept Comp Sci, Riyadh 11432, Saudi Arabia
[2] King Saud Univ, Coll Comp & Informat Sci, Dept Informat Technol, Riyadh 11451, Saudi Arabia
来源：
INFORMATION | 2022年 / 13卷 / 04期
关键词：
recommendation system; location-based social networks (LBSNs); matrix factorization (MF); points of interest (POIs); SINGULAR-VALUE DECOMPOSITION; MATRIX FACTORIZATION; MODEL;
D O I：
10.3390/info13040188
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Currently, social networks allow individuals from all over the world to share ideas, activities, events, and interests over the Internet. Using location-based social networks (LBSNs), users can share their locations and location-related content, including images and reviews. Location rec-14 recommendation system-based LBSN has gained considerable attention in research using techniques and methods based on users' geosocial activities. In this study, we present a comparative analysis of three matrix factorization (MF) algorithms, namely, singular value decomposition (SVD), singular value decomposition plus (SVD++), and nonnegative matrix factorization (NMF). The primary task of the implemented recommender system was to predict restaurant ratings for each user and make a recommendation based on this prediction. This experiment used two performance metrics for evaluation, namely, root mean square error (RMSE) and mean absolute error (MAE). The RMSEs confirmed the efficacy of SVD with a lower error rate, whereas SVD++ had a lower error rate in terms of MAE.
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页数：12
相关论文
共 40 条
[1]  
[Anonymous], 2008, P 14 ACM SIGKDD INT, DOI DOI 10.1145/1401890.1401944
[2]   Personalized, Affect and Performance-driven Computer-based Learning [J].
Athanasiadis, Christos ;
Hortal, Enrique ;
Koutsoukos, Dimitrios ;
Lens, Carmen Zarco ;
Asteriadis, Stylianos .
PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION (CSEDU), VOL 1, 2017, :132-139
[3]   Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition [J].
Bakir, Cigdem .
TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2018, 25 (01) :130-135
[4]   Using linear algebra for intelligent information retrieval [J].
Berry, MW ;
Dumais, ST ;
OBrien, GW .
SIAM REVIEW, 1995, 37 (04) :573-595
[5]   Recommender Systems Clustering Using Bayesian Non Negative Matrix Factorization [J].
Bobadilla, Jesus ;
Bojorque, Rodolfo ;
Hernando Esteban, Antonio ;
Hurtado, Remigio .
IEEE ACCESS, 2018, 6 :3549-3564
[6]   Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey [J].
Bokde, Dheeraj ;
Girase, Sheetal ;
Mukhopadhyay, Debajyoti .
PROCEEDINGS OF 4TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND CONTROL(ICAC3'15), 2015, 49 :136-146
[7]   Integrating spatial and temporal contexts into a factorization model for POI recommendation [J].
Cai, Ling ;
Xu, Jun ;
Liu, Ju ;
Pei, Tao .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2018, 32 (03) :524-546
[8]   Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature [J].
Chai, T. ;
Draxler, R. R. .
GEOSCIENTIFIC MODEL DEVELOPMENT, 2014, 7 (03) :1247-1250
[9]   Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization [J].
Chai, Zheng-Yi ;
Li, Ya-Lun ;
Han, Ya-Min ;
Zhu, Si-Feng .
IEEE ACCESS, 2019, 7 :6060-6071
[10]   A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks [J].
Chen, Rui ;
Hua, Qingyi ;
Chang, Yan-Shuo ;
Wang, Bo ;
Zhang, Lei ;
Kong, Xiangjie .
IEEE ACCESS, 2018, 6 :64301-64320
1234