@inproceedings{he2011citation,
  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.},
  acmid = {1935926},
  address = {New York, NY, USA},
  author = {He, Qi and Kifer, Daniel and Pei, Jian and Mitra, Prasenjit and Giles, C. Lee},
  booktitle = {Proceedings of the fourth ACM international conference on Web search and data mining},
  doi = {10.1145/1935826.1935926},
  interhash = {7e98aaf26a7ed6cc624249a3ab570d7a},
  intrahash = {bbd320f03d13c6cfff4b6f9e6b4630f7},
  isbn = {978-1-4503-0493-1},
  location = {Hong Kong, China},
  numpages = {10},
  pages = {755--764},
  publisher = {ACM},
  title = {Citation recommendation without author supervision},
  url = {http://doi.acm.org/10.1145/1935826.1935926},
  year = 2011
}

@inproceedings{he2010contextaware,
  abstract = {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.},
  acmid = {1772734},
  address = {New York, NY, USA},
  author = {He, Qi and Pei, Jian and Kifer, Daniel and Mitra, Prasenjit and Giles, Lee},
  booktitle = {Proceedings of the 19th international conference on World wide web},
  doi = {10.1145/1772690.1772734},
  interhash = {d48586d4ee897859c5d797e671f3e384},
  intrahash = {17f7aa5c8bf1d9055fd83688f46fde65},
  isbn = {978-1-60558-799-8},
  location = {Raleigh, North Carolina, USA},
  numpages = {10},
  pages = {421--430},
  publisher = {ACM},
  title = {Context-aware citation recommendation},
  url = {http://doi.acm.org/10.1145/1772690.1772734},
  year = 2010
}