Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Propagation of trust and distrust
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Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
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ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Spectral clustering and transductive learning with multiple views
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Discovering Temporal Communities from Social Network Documents
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Web document clustering using hyperlink structures
Computational Statistics & Data Analysis
Learning latent semantic relations from clickthrough data for query suggestion
Proceedings of the 17th ACM conference on Information and knowledge management
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Probabilistic polyadic factorization and its application to personalized recommendation
Proceedings of the 17th ACM conference on Information and knowledge management
Effective latent space graph-based re-ranking model with global consistency
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Connections between the lines: augmenting social networks with text
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Catching the drift: learning broad matches from clickthrough data
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Proceedings of the 18th ACM conference on Information and knowledge management
iOLAP: A framework for analyzing the internet, social networks, and other networked data
IEEE Transactions on Multimedia - Special section on communities and media computing
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Incremental all pairs similarity search for varying similarity thresholds
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Context-aware citation recommendation
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Multi task learning on multiple related networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Unified tag analysis with multi-edge graph
Proceedings of the international conference on Multimedia
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
Citation recommendation without author supervision
Proceedings of the fourth ACM international conference on Web search and data mining
Improving Recommender Systems by Incorporating Social Contextual Information
ACM Transactions on Information Systems (TOIS)
Contextual Video Recommendation by Multimodal Relevance and User Feedback
ACM Transactions on Information Systems (TOIS)
Like like alike: joint friendship and interest propagation in social networks
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Probabilistic factor models for web site recommendation
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommending citations with translation model
Proceedings of the 20th ACM international conference on Information and knowledge management
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Modeling and exploiting heterogeneous bibliographic networks for expertise ranking
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Interest prediction on multinomial, time-evolving social graphs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Proceedings of the 20th ACM international conference on Multimedia
Position-Aligned translation model for citation recommendation
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
User community discovery from multi-relational networks
Decision Support Systems
Recommendation in Online Health Communities
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Multi-space probabilistic sequence modeling
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A unified graph model for personalized query-oriented reference paper recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
Understanding and promoting micro-finance activities in Kiva.org
Proceedings of the 7th ACM international conference on Web search and data mining
The ACL anthology network corpus
Language Resources and Evaluation
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The Web offers rich relational data with different semantics. In this paper, we address the problem of document recommendation in a digital library, where the documents in question are networked by citations and are associated with other entities by various relations. Due to the sparsity of a single graph and noise in graph construction, we propose a new method for combining multiple graphs to measure document similarities, where different factorization strategies are used based on the nature of different graphs. In particular, the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space. Based on the obtained embedding, a new recommendation framework is developed using semi-supervised learning on graphs. In addition, we address the scalability issue and propose an incremental algorithm. The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only. The new batch and incremental methods are evaluated on two real world datasets prepared from CiteSeer. Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method.