The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ACM Transactions on Information Systems (TOIS)
Web path recommendations based on page ranking and Markov models
Proceedings of the 7th annual ACM international workshop on Web information and data management
Web site personalization based on link analysis and navigational patterns
ACM Transactions on Internet Technology (TOIT)
Automatic metadata generation using associative networks
ACM Transactions on Information Systems (TOIS)
Predicting labels for dyadic data
Data Mining and Knowledge Discovery
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A supervised machine learning link prediction approach for tag recommendation
OCSC'11 Proceedings of the 4th international conference on Online communities and social computing
Link prediction via matrix factorization
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Citation count prediction: learning to estimate future citations for literature
Proceedings of the 20th ACM international conference on Information and knowledge management
Incorporating context into recommender systems: an empirical comparison of context-based approaches
Electronic Commerce Research
Supervised rank aggregation approach for link prediction in complex networks
Proceedings of the 21st international conference companion on World Wide Web
Who will be participating next?: predicting the participation of Dark Web community
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
Link Prediction for Bipartite Social Networks: The Role of Structural Holes
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Link Prediction: Fair and Effective Evaluation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Enhancing Academic Event Participation with Context-aware and Social Recommendations
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Folksonomy link prediction based on a tripartite graph for tag recommendation
Journal of Intelligent Information Systems
Group and link analysis of multi-relational scientific social networks
Journal of Systems and Software
Proceedings of the 7th ACM conference on Recommender systems
Computationally efficient link prediction in a variety of social networks
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
Internal link prediction: A new approach for predicting links in bipartite graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
| Hi-index | 0.00 |
Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms.