GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Studying Recommendation Algorithms by Graph Analysis
Journal of Intelligent Information Systems
Introduction to recommender systems: Algorithms and Evaluation
ACM Transactions on Information Systems (TOIS)
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Small Worlds Among Interlocking Directors: Network Structure and Distance in Bipartite Graphs
Computational & Mathematical Organization Theory
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
IEEE Transactions on Knowledge and Data Engineering
A study of mixture models for collaborative filtering
Information Retrieval
Guest Editors' Introduction: Recommender Systems
IEEE Intelligent Systems
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
A clustering coefficient for weighted networks, with application to gene expression data
AI Communications - Network Analysis in Natural Sciences and Engineering
The adaptive web: methods and strategies of web personalization
The adaptive web: methods and strategies of web personalization
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Active learning driven by rating impact analysis
Proceedings of the fourth ACM conference on Recommender systems
Latent subject-centered modeling of collaborative tagging: An application in social search
ACM Transactions on Management Information Systems (TMIS)
Impact of data characteristics on recommender systems performance
ACM Transactions on Management Information Systems (TMIS)
Recommendation in reciprocal and bipartite social networks: a case study of online dating
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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A large number of collaborative filtering algorithms have been proposed in the literature as the foundation 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 recommendation model/algorithm validation and selection issues based on the graph topological modeling methodology. By representing the input data in the form of consumer--product interactions as a bipartite graph, the consumer--product graph, we develop bipartite graph topological measures to capture patterns that exist in the input data relevant to the transaction-based recommendation task. We observe the deviations of these topological measures of real-world consumer--product graphs from the expected values for simulated random bipartite graphs. These deviations help explain why certain collaborative filtering algorithms work for particular recommendation data sets. They can also serve as the basis for a comprehensive model selection framework that “recommends” appropriate collaborative filtering algorithms given characteristics of the data set under study. We validate our approach using three real-world recommendation data sets and demonstrate the effectiveness of the proposed bipartite graph topological measures in selection and validation of commonly used heuristic-based recommendation algorithms, the user-based, item-based, and graph-based algorithms.