Content-Based Citation Recommendation

@inproceedings{Bhagavatula2018ContentBasedCR,
  title={Content-Based Citation Recommendation},
  author={Chandra Bhagavatula and Sergey Feldman and Russell Power and Waleed Ammar},
  booktitle={NAACL},
  year={2018}
}
We present a content-based method for recommending citations in an academic paper draft. [] Key Method Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and over 22% in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it…

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References

SHOWING 1-10 OF 28 REFERENCES

Context-Based Collaborative Filtering for Citation Recommendation

TLDR
The proposed citation recommendation method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list.

An Analysis of Citation Recommender Systems: Beyond the Obvious

TLDR
This paper considers the problem of citation recommendation by extending a set of known-to-be-relevant references and proposes to combine author, venue and keyword information to interpret the citation behavior behind loosely connected papers.

ClusCite: effective citation recommendation by information network-based clustering

TLDR
A novel cluster-based citation recommendation framework, called ClusCite, which explores the principle that citations tend to be softly clustered into interest groups based on multiple types of relationships in the network, and learns group memberships for objects and the significance of relevance features for each interest group by solving a joint optimization problem.

Citation recommendation without author supervision

TLDR
A user simply inputs a query manuscript (without a bibliography) and the system automatically finds locations where citations are needed, and it is shown that naïve approaches do not work well due to massive noise in the document corpus.

Recommending citations: translating papers into references

TLDR
This work proposes a method that "translates" research papers into references by considering the citations and their contexts from existing papers as parallel data written in two different "languages" using the translation model to create a relationship between these two "vocabularies".

A Neural Probabilistic Model for Context Based Citation Recommendation

TLDR
A novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers is proposed that significantly outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG.

A Discriminative Approach to Topic-Based Citation Recommendation

TLDR
A two-layer Restricted Boltzmann Machine model, called RBM-CS, is proposed, which can discover topic distributions of paper content and citation relationship simultaneously and can significantly outperform baseline methods for citation recommendation.

A Query-oriented Approach for Relevance in Citation Networks

TLDR
A two-stage query-dependent approach for retrieving relevant papers given a keyword-based query that allows for recommendations that are both highly authoritative, and also textually related to the query.

Context-aware citation recommendation

TLDR
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 and implement a prototype system in CiteSeerX.

Citation Prediction in Heterogeneous Bibliographic Networks

TLDR
A meta-path based prediction model on a topic discriminative search space is built and a two-phase citation probability learning approach is proposed, in order to predict citation relationship effectively and efficiently.