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

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