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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
The Journal of Machine Learning Research
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
TSSP: A Reinforcement Algorithm to Find Related Papers
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A basis for information retrieval in context
ACM Transactions on Information Systems (TOIS)
Learning multiple graphs for document recommendations
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Joint latent topic models for text and citations
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Technical paper recommendation: a study in combining multiple information sources
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A recommender system based on tag and time information for social tagging systems
Expert Systems with Applications: An International Journal
Citation recommendation without author supervision
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CollabSeer: a search engine for collaboration discovery
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A source independent framework for research paper recommendation
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Citation count prediction: learning to estimate future citations for literature
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Adding structure to top-k: from items to expansions
Proceedings of the 20th ACM international conference on Information and knowledge management
Recommending citations with translation model
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A recommender system for assistive environments
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Positive influence dominating set in e-learning social networks
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
Document hierarchies from text and links
Proceedings of the 21st international conference on World Wide Web
Similar researcher search in academic environments
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Context-Aware personalized search based on user and resource profiles in folksonomies
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Plink-LDA: using link as prior information in topic modeling
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Improving MeSH classification of biomedical articles using citation contexts
Journal of Biomedical Informatics
Recommending citations: translating papers into references
Proceedings of the 21st ACM international conference on Information and knowledge management
Fast ALS-Based tensor factorization for context-aware recommendation from implicit feedback
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Position-Aligned translation model for citation recommendation
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
Context-aware item-to-item recommendation within the factorization framework
Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation
Exploiting potential citation papers in scholarly paper recommendation
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Ranking experts using author-document-topic graphs
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Can't see the forest for the trees?: a citation recommendation system
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Academic network analysis: a joint topic modeling approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Towards a personalized, scalable, and exploratory academic recommendation service
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Leveraging the citation graph to recommend keywords
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A unified graph model for personalized query-oriented reference paper recommendation
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Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
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When you write papers, how many times do you want to make some citations at a place but you are not sure which papers to cite? Do you wish to have a recommendation system which can recommend a small number of good candidates for every place that you want to make some citations? In this paper, we present our initiative of building a context-aware citation recommendation system. High quality citation recommendation is challenging: not only should the citations recommended be relevant to the paper under composition, but also should match the local contexts of the places citations are made. Moreover, it is far from trivial to model how the topic of the whole paper and the contexts of the citation places should affect the selection and ranking of citations. To tackle the problem, we develop a context-aware approach. The core idea is to design a novel non-parametric probabilistic model which can measure the context-based relevance between a citation context and a document. Our approach can recommend citations for a context effectively. Moreover, it can recommend a set of citations for a paper with high quality. We implement a prototype system in CiteSeerX. An extensive empirical evaluation in the CiteSeerX digital library against many baselines demonstrates the effectiveness and the scalability of our approach.