Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (<i>without</i> a bibliography) and our system automatically finds locations where citations are needed. We show that na&#239;ve approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective.

@inproceedings{he2011citation, author = {He, Qi and Kifer, Daniel and Pei, Jian and Mitra, Prasenjit and Giles, C. Lee}, title = {Citation recommendation without author supervision}, booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining}, publisher = {ACM}, address = {New York, NY, USA}, year = {2011}, pages = {755--764}, url = {http://doi.acm.org/10.1145/1935826.1935926}, doi = {10.1145/1935826.1935926}, isbn = {978-1-4503-0493-1}, keywords = {analysis, citation, item, paper, recommender, research}, abstract = {Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (<i>without</i> a bibliography) and our system automatically finds locations where citations are needed. We show that na&#239;ve approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective.} }

%0 = inproceedings %A = He, Qi and Kifer, Daniel and Pei, Jian and Mitra, Prasenjit and Giles, C. Lee %B = Proceedings of the fourth ACM international conference on Web search and data mining %C = New York, NY, USA %D = 2011 %I = ACM %T = Citation recommendation without author supervision %U = http://doi.acm.org/10.1145/1935826.1935926

Scientists depend on literature search to find prior work that is relevant to their research ideas. We introduce a retrieval model for literature search that incorporates a wide variety of factors important to researchers, and learns the weights of each of these factors by observing citation patterns. We introduce features like topical similarity and author behavioral patterns, and combine these with features from related work like citation count and recency of publication. We present an iterative process for learning weights for these features that alternates between retrieving articles with the current retrieval model, and updating model weights by training a supervised classifier on these articles. We propose a new task for evaluating the resulting retrieval models, where the retrieval system takes only an abstract as its input and must produce as output the list of references at the end of the abstract's article. We evaluate our model on a collection of journal, conference and workshop articles from the ACL Anthology Reference Corpus. Our model achieves a mean average precision of 28.7, a 12.8 point improvement over a term similarity baseline, and a significant improvement both over models using only features from related work and over models without our iterative learning.

@inproceedings{bethard2010should, author = {Bethard, Steven and Jurafsky, Dan}, title = {Who should I cite: learning literature search models from citation behavior}, booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management}, publisher = {ACM}, address = {New York, NY, USA}, year = {2010}, pages = {609--618}, url = {http://doi.acm.org/10.1145/1871437.1871517}, doi = {10.1145/1871437.1871517}, isbn = {978-1-4503-0099-5}, keywords = {analysis, citation, item, paper, recommender, research}, abstract = {Scientists depend on literature search to find prior work that is relevant to their research ideas. We introduce a retrieval model for literature search that incorporates a wide variety of factors important to researchers, and learns the weights of each of these factors by observing citation patterns. We introduce features like topical similarity and author behavioral patterns, and combine these with features from related work like citation count and recency of publication. We present an iterative process for learning weights for these features that alternates between retrieving articles with the current retrieval model, and updating model weights by training a supervised classifier on these articles. We propose a new task for evaluating the resulting retrieval models, where the retrieval system takes only an abstract as its input and must produce as output the list of references at the end of the abstract's article. We evaluate our model on a collection of journal, conference and workshop articles from the ACL Anthology Reference Corpus. Our model achieves a mean average precision of 28.7, a 12.8 point improvement over a term similarity baseline, and a significant improvement both over models using only features from related work and over models without our iterative learning.} }

%0 = inproceedings %A = Bethard, Steven and Jurafsky, Dan %B = Proceedings of the 19th ACM international conference on Information and knowledge management %C = New York, NY, USA %D = 2010 %I = ACM %T = Who should I cite: learning literature search models from citation behavior %U = http://doi.acm.org/10.1145/1871437.1871517

Classical papers are of great help for beginners to get familiar with a new research area. However, digging them out is a difficult problem. This paper proposes Claper, a novel academic recommendation system based on two proven principles: the Principle of Download Persistence and the Principle of Citation Approaching (we prove them based on real-world datasets). The principle of download persistence indicates that classical papers have few decreasing download frequencies since they were published. The principle of citation approaching indicates that a paper which cites a classical paper is likely to cite citations of that classical paper. Our experimental results based on large-scale real-world datasets illustrate Claper can effectively recommend classical papers of high quality to beginners and thus help them enter their research areas.

@inproceedings{wang2010claper, author = {Wang, Yonggang and Zhai, Ennan and Hu, Jianbin and Chen, Zhong}, title = {Claper: Recommend classical papers to beginners}, booktitle = {Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery}, publisher = {IEEE}, year = {2010}, volume = {6}, pages = {2777--2781}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227}, doi = {10.1109/FSKD.2010.5569227}, keywords = {analysis, citation, item, paper, recommender, research}, abstract = {Classical papers are of great help for beginners to get familiar with a new research area. However, digging them out is a difficult problem. This paper proposes Claper, a novel academic recommendation system based on two proven principles: the Principle of Download Persistence and the Principle of Citation Approaching (we prove them based on real-world datasets). The principle of download persistence indicates that classical papers have few decreasing download frequencies since they were published. The principle of citation approaching indicates that a paper which cites a classical paper is likely to cite citations of that classical paper. Our experimental results based on large-scale real-world datasets illustrate Claper can effectively recommend classical papers of high quality to beginners and thus help them enter their research areas.} }

%0 = inproceedings %A = Wang, Yonggang and Zhai, Ennan and Hu, Jianbin and Chen, Zhong %B = Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery %D = 2010 %I = IEEE %T = Claper: Recommend classical papers to beginners %U = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227

We approach the problem of academic literature search by considering an unpublished manuscript as a query to a search system. We use the text of previous literature as well as the citation graph that connects it to find relevant related material. We evaluate our technique with manual and automatic evaluation methods, and find an order of magnitude improvement in mean average precision as compared to a text similarity baseline.

@inproceedings{Strohman:2007:RCA:1277741.1277868, author = {Strohman, Trevor and Croft, W. Bruce and Jensen, David}, title = {Recommending citations for academic papers}, booktitle = {Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval}, publisher = {ACM}, address = {New York, NY, USA}, year = {2007}, pages = {705--706}, url = {http://doi.acm.org/10.1145/1277741.1277868}, doi = {10.1145/1277741.1277868}, isbn = {978-1-59593-597-7}, keywords = {analysis, citation, item, paper, recommender, research}, abstract = {We approach the problem of academic literature search by considering an unpublished manuscript as a query to a search system. We use the text of previous literature as well as the citation graph that connects it to find relevant related material. We evaluate our technique with manual and automatic evaluation methods, and find an order of magnitude improvement in mean average precision as compared to a text similarity baseline.} }

%0 = inproceedings %A = Strohman, Trevor and Croft, W. Bruce and Jensen, David %B = Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval %C = New York, NY, USA %D = 2007 %I = ACM %T = Recommending citations for academic papers %U = http://doi.acm.org/10.1145/1277741.1277868

@article{loo2002additive, author = {Loo, Yueh-Lin and Willett, Robert L. and Baldwin, Kirk W. and Rogers, John A.}, title = {Additive, nanoscale patterning of metal films with a stamp and a surface chemistry mediated transfer process: Applications in plastic electronics}, journal = {Applied Physics Letters}, publisher = {AIP}, year = {2002}, volume = {81}, number = {3}, pages = {562-564}, url = {http://link.aip.org/link/?APL/81/562/1}, doi = {10.1063/1.1493226}, keywords = {display, e-ink, nano, paper} }

%0 = article %A = Loo, Yueh-Lin and Willett, Robert L. and Baldwin, Kirk W. and Rogers, John A. %D = 2002 %I = AIP %T = Additive, nanoscale patterning of metal films with a stamp and a surface chemistry mediated transfer process: Applications in plastic electronics %U = http://link.aip.org/link/?APL/81/562/1