GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering
DNIS '02 Proceedings of the Second International Workshop on Databases in Networked Information Systems
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Collaborative Filtering Using a Regression-Based Approach
Knowledge and Information Systems
Collaborative Filtering with Maximum Entropy
IEEE Intelligent Systems
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Protein function prediction via graph kernels
Bioinformatics
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Context-Aware SVM for Context-Dependent Information Recommendation
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Usage derived recommendations for a video digital library
Journal of Network and Computer Applications
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
IEEE Transactions on Knowledge and Data Engineering
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Website browsing aid: A navigation graph-based recommendation system
Decision Support Systems
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Local Probabilistic Models for Link Prediction
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Recommendation Method for Improving Customer Lifetime Value
IEEE Transactions on Knowledge and Data Engineering
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Recommender system based on workflow
Decision Support Systems
Journal of Management Information Systems
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Gene function prediction with gene interaction networks: a context graph kernel approach
IEEE Transactions on Information Technology in Biomedicine
The link prediction problem in bipartite networks
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
One-Class support vector machines for recommendation tasks
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A constrained spreading activation approach to collaborative filtering
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
A Graphical Model for Context-Aware Visual Content Recommendation
IEEE Transactions on Multimedia
Collaborative user modeling for enhanced content filtering in recommender systems
Decision Support Systems
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Recommender systems have been widely adopted in online applications to suggest products, services, and contents to potential users. Collaborative filtering (CF) is a successful recommendation paradigm that employs transaction information to enrich user and item features for recommendation. By mapping transactions to a bipartite user-item interaction graph, a recommendation problem is converted into a link prediction problem, where the graph structure captures subtle information on relations between users and items. To take advantage of the structure of this graph, we propose a kernel-based recommendation approach and design a novel graph kernel that inspects customers and items (indirectly) related to the focal user-item pair as its context to predict whether there may be a link. In the graph kernel, we generate random walk paths starting from a focal user-item pair and define similarities between user-item pairs based on the random walk paths. We prove the validity of the kernel and apply it in a one-class classification framework for recommendation. We evaluate the proposed approach with three real-world datasets. Our proposed method outperforms state-of-the-art benchmark algorithms, particularly when recommending a large number of items. The experiments show the necessity of capturing user-item graph structure in recommendation.