CASIA OpenIR  > 互联网大数据与安全信息
Why Does Collaborative Filtering Work? Transaction-based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs,
Huang, Zan1; Zeng, Daniel2
Source PublicationINFORMS Journal on Computing
2011
Volume23Issue:1Pages:138-152
Subtype期刊论文
Abstract

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.

Keyword
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23212
Collection互联网大数据与安全信息
Affiliation1.Pennsylvania State University
2.The University of Arizona
Recommended Citation
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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