Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Training products of experts by minimizing contrastive divergence
Neural Computation
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A fast learning algorithm for deep belief nets
Neural Computation
Recommending citations for academic papers
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Who should I cite: learning literature search models from citation behavior
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Citation recommendation without author supervision
Proceedings of the fourth ACM international conference on Web search and data mining
Recommending citations with translation model
Proceedings of the 20th ACM international conference on Information and knowledge management
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
Recommending citations: translating papers into references
Proceedings of the 21st ACM international conference on Information and knowledge management
Can't see the forest for the trees?: a citation recommendation system
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Hi-index | 0.00 |
In this paper, we present a study of a novel problem, i.e. topic-based citation recommendation, which involves recommending papers to be referred to. Traditionally, this problem is usually treated as an engineering issue and dealt with using heuristics. This paper gives a formalization of topic-based citation recommendation and proposes a discriminative approach to this problem. Specifically, it proposes a two-layer Restricted Boltzmann Machine model, called RBM-CS, which can discover topic distributions of paper content and citation relationship simultaneously. Experimental results demonstrate that RBM-CS can significantly outperform baseline methods for citation recommendation.