Why Does Collaborative Filtering Work? Transaction-based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs,
Huang, Zan1; Zeng, Daniel2
发表期刊INFORMS Journal on Computing
2011
卷号23期号:1页码:138-152
文章类型期刊论文
摘要

A large number of collaborative filtering (CF) algorithms have been proposed in the literature as the core
of automated recommender systems. However, the underlying justification for these algorithms is lacking
and their relative performances are typically domain- and data-dependent. In this paper, we aim to
develop initial understanding of the validation and model/algorithm selection issues based on the graph
topological modeling methodology. By representing the input data in the form of consumer-product
interactions such as purchases and ratings as a bipartite graph, we develop bipartite graph topological
measures to capture patterns that exist in the input data relevant to recommendation. Using a simulation
approach, we observe the deviations of these topological measures for given recommendation datasets
from the expected values for simulated random datasets. These deviations help explain why certain CF
algorithms work for the given datasets. They can also serve as the basis for a comprehensive model
selection framework that chooses appropriate CF algorithms given the characteristics of the dataset under
study. We validate our approach using two real-world e-commerce datasets.

关键词
收录类别SCI
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/23212
专题互联网大数据与安全信息学研究中心
作者单位1.Pennsylvania State University
2.The University of Arizona
推荐引用方式
GB/T 7714
Huang, Zan,Zeng, Daniel. Why Does Collaborative Filtering Work? Transaction-based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs,[J]. INFORMS Journal on Computing,2011,23(1):138-152.
APA Huang, Zan,&Zeng, Daniel.(2011).Why Does Collaborative Filtering Work? Transaction-based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs,.INFORMS Journal on Computing,23(1),138-152.
MLA Huang, Zan,et al."Why Does Collaborative Filtering Work? Transaction-based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs,".INFORMS Journal on Computing 23.1(2011):138-152.
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