Adaptive Collaborative Filtering Based on Scalable Clustering for Big Recommender Systems

被引：0

作者：

Lee, O-Joun ^{[1
]}

Hong, Min-Sung ^{[1
]}

Jung, Jason J. ^{[1
]}

Shin, Juhyun ^{[2
]}

Kim, Pankoo ^{[3
]}

机构：

[1] Chung Ang Univ, Sch Comp Engn, KS013, Seoul 156756, South Korea

[2] Chosun Univ, Dept Control & Measuring Robot Engn, KS008, Kwangju 501759, South Korea

[3] Chosun Univ, Dept Comp Engn, KS008, Kwangju 501759, South Korea

来源：

ACTA POLYTECHNICA HUNGARICA | 2016年 / 13卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

Big data; Recommender System; Adaptive System; Clustering-based Collaborative Filtering; Scalable System;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The large amount of information that is currently being collected (the so-called "big data"), have resulted in model-based Collaborative Filtering (CF) methods to encountering limitations, e.g., the sparsity problem and the scalability problem. It is difficult for model-based CF methods to address the scalability-performance trade-off. Therefore, we propose a scalable clustering-based CF method in this paper that can help provide a balance by re-locating elements in the cluster model. The proposed method is evaluated by performing a comparison against existing methods in terms of measurements for the Mean Absolute Error (MAE) and response time to assess the performance and scalability. The experimental results show that the proposed method improves the MAE and the response time by 50.79% and 48.25%, respectively.

引用

页码：179 / 194

页数：16

共 50 条

[1] A Scalable Collaborative Filtering Based Recommender System Using Incremental Clustering
Chakraborty, Partha Sarathi
[J]. 2009 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE, VOLS 1-3, 2009, : 1526 - 1529
[2] Hierarchical Clustering for Collaborative Filtering Recommender Systems
Inga Chalco, Cesar
Bojorque Chasi, Rodolfo
Hurtado Ortiz, Remigio
[J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, SOFTWARE AND SYSTEMS ENGINEERING, 2019, 787 : 346 - 356
[3] Scalable Collaborative Filtering Approaches for Large Recommender Systems
Takacs, Gabor
Pilaszy, Istvan
Nemeth, Bottyan
Tikk, Domonkos
[J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2009, 10 : 623 - 656
[4] Fuzzy clustering with optimization for collaborative filtering-based recommender systems
Soojung Lee
[J]. Journal of Ambient Intelligence and Humanized Computing, 2022, 13 : 4189 - 4206
[5] Fuzzy clustering with optimization for collaborative filtering-based recommender systems
Lee, Soojung
[J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2021, 13 (9) : 4189 - 4206
[6] Multi-Clustering in Fast Collaborative Filtering Recommender Systems
Kuzelewska, Urszula
[J]. THIRTEENTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING ADVANCES (ICSEA 2018), 2018, : 85 - 90
[7] Tag Based Collaborative Filtering for Recommender Systems
Liang, Huizhi
Xu, Yue
Li, Yuefeng
Nayak, Richi
[J]. ROUGH SETS AND KNOWLEDGE TECHNOLOGY, PROCEEDINGS, 2009, 5589 : 666 - 673
[8] Hybrid Recommender System based on Fuzzy Clustering and Collaborative Filtering
Verma, Sumit Kumar
Mittal, Namita
Agarwal, Basant
[J]. 2013 4TH IEEE INTERNATIONAL CONFERENCE ON COMPUTER & COMMUNICATION TECHNOLOGY (ICCCT), 2013, : 116 - 120
[9] Recommender Systems and Collaborative Filtering
Ortega, Fernando
Gonzalez-Prieto, Angel
[J]. APPLIED SCIENCES-BASEL, 2020, 10 (20):
[10] Adaptive Collaborative Filtering for Recommender System
An La
Phuong Vo
Tu Vu
[J]. GRAPH-BASED REPRESENTATION AND REASONING (ICCS 2019), 2019, 11530 : 117 - 130

← 1 2 3 4 5 →