@inproceedings{puertamelguizo2008personalized,
  abstract = {Writing is a complex task and several computer systems have been developed in order to support writing. Most of these systems, however, are mainly designed with the purpose of supporting the processes of planning, organizing and connecting ideas. In general, these systems help writers to formulate external visual representations of their ideas and connections of the main topics that should be addressed in the paper, sequence of the sections, etc. With the advent of the world wide web, writing and finding information for the written text has become increasingly intertwined. Consequently, it is necessary to develop systems able to support the task of finding relevant information during writing, without interfering with the writing process proper. In this paper we present the Proactive Recommender System: A propos. This system is being developed in order to support writers in the difficult task of finding appropriate relevant information during writing. We raise the question whether the tendency to interleave (re)search and writing implies a need for developing more comprehensive models of the cognitive processes involved in writing scientific and policy papers. },
  author = {Puerta Melguizo, Mari Carmen and Muñoz Ramos, Olga and Boves, Lou and Bogers, Toine and van den Bosch, Antal},
  booktitle = {Proceedings of the Workshop on Natural Language Processing resources, algorithms and tools for authoring aids},
  editor = {and},
  interhash = {264e2eeb8c9417f8dc974d22e5502ae9},
  intrahash = {5a29342d397e1c4e7f783029fc134620},
  pages = {21--26},
  title = {A Personalized Recommender System for Writing in the Internet Age },
  url = {http://repository.dlsi.ua.es/251/1/workshops/W23_Proceedings.pdf#page=27},
  year = 2008
}

@inproceedings{bogers2008recommending,
  abstract = {We describe the use of the social reference management website CiteULike for recommending scientific articles to users, based on their reference library. We test three different collaborative filtering algorithms, and find that user-based filtering performs best. A temporal analysis of the data indexed by CiteULike shows that it takes about two years for the cold-start problem to disappear and recommendation performance to improve.},
  acmid = {1454053},
  address = {New York, NY, USA},
  author = {Bogers, Toine and van den Bosch, Antal},
  booktitle = {Proceedings of the 2008 ACM Conference on Recommender Systems},
  doi = {10.1145/1454008.1454053},
  interhash = {692eb1215676da39997ad861b681c450},
  intrahash = {a1c3f0b4a9bd5273ffd298128590598a},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  numpages = {4},
  pages = {287--290},
  publisher = {ACM},
  title = {Recommending scientific articles using citeulike},
  url = {http://doi.acm.org/10.1145/1454008.1454053},
  year = 2008
}

@inproceedings{1454053,
  address = {New York, NY, USA},
  author = {Bogers, Toine and van den Bosch, Antal},
  booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
  doi = {http://doi.acm.org/10.1145/1454008.1454053},
  interhash = {692eb1215676da39997ad861b681c450},
  intrahash = {9d0d8ca850db6cf6177efc66e16785b7},
  isbn = {978-1-60558-093-7},
  location = {Lausanne, Switzerland},
  pages = {287--290},
  publisher = {ACM},
  title = {Recommending scientific articles using citeulike},
  url = {http://portal.acm.org/citation.cfm?id=1454053},
  year = 2008
}