@preprint{beel2013research,
  author = {Beel, Joeran and Langer, Stefan and Genzmehr, Marcel and Gipp, Bela and Breitinger, Corinna and Nürnberger, Andreas},
  interhash = {544758b1fd737c010643f529c4f48ae6},
  intrahash = {4afa2bd342dda6b6d32713aa0fbc33bd},
  title = {Research Paper Recommender System Evaluation: A Quantitative Literature Survey},
  year = 2013
}

@inproceedings{ls_leimeister,
  address = {Orlando, Florida, USA},
  author = {Söllner, Matthias and Pavlou, Paul and Leimeister, Jan Marco},
  booktitle = {Academy of Management Annual Meeting},
  interhash = {71fc731ea8fd7d6893422d5ed0e8635b},
  intrahash = {0f499f764a9a2170c784614f7e9c06df},
  title = {Understanding Trust in IT Artifacts – A new Conceptual Approach},
  year = 2013
}

@article{pham2011development,
  abstract = {In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.},
  address = {Wien},
  affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany},
  author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias},
  doi = {10.1007/s13278-011-0024-x},
  interhash = {193312234ed176aa8be9f35d4d1c4e72},
  intrahash = {8ae08cacda75da80bfa5604cfce48449},
  issn = {1869-5450},
  journal = {Social Network Analysis and Mining},
  keyword = {Computer Science},
  number = 4,
  pages = {321--340},
  publisher = {Springer},
  title = {Development of computer science disciplines: a social network analysis approach},
  url = {http://dx.doi.org/10.1007/s13278-011-0024-x},
  volume = 1,
  year = 2011
}

@inproceedings{wang2010claper,
  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.},
  author = {Wang, Yonggang and Zhai, Ennan and Hu, Jianbin and Chen, Zhong},
  booktitle = {Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery},
  doi = {10.1109/FSKD.2010.5569227},
  interhash = {7180ddaf1c1765a45fd244027bd0bf43},
  intrahash = {7da72bf2f0538afad9377a0d50c263b4},
  month = aug,
  pages = {2777--2781},
  publisher = {IEEE},
  title = {Claper: Recommend classical papers to beginners},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227},
  volume = 6,
  year = 2010
}

@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{bethard2010should,
  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.},
  acmid = {1871517},
  address = {New York, NY, USA},
  author = {Bethard, Steven and Jurafsky, Dan},
  booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management},
  doi = {10.1145/1871437.1871517},
  interhash = {1cdf6c7da38af251279e9fb915266af2},
  intrahash = {369206c7472baeaa5ecefef586e16c6a},
  isbn = {978-1-4503-0099-5},
  location = {Toronto, ON, Canada},
  numpages = {10},
  pages = {609--618},
  publisher = {ACM},
  title = {Who should I cite: learning literature search models from citation behavior},
  url = {http://doi.acm.org/10.1145/1871437.1871517},
  year = 2010
}

@inproceedings{Strohman:2007:RCA:1277741.1277868,
  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.},
  acmid = {1277868},
  address = {New York, NY, USA},
  author = {Strohman, Trevor and Croft, W. Bruce and Jensen, David},
  booktitle = {Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval},
  doi = {10.1145/1277741.1277868},
  interhash = {a34279add7d7a9f3c564735b7b8dcd44},
  intrahash = {7a0b1ff2a40b3989ef8d83daabd91159},
  isbn = {978-1-59593-597-7},
  location = {Amsterdam, The Netherlands},
  numpages = {2},
  pages = {705--706},
  publisher = {ACM},
  title = {Recommending citations for academic papers},
  url = {http://doi.acm.org/10.1145/1277741.1277868},
  year = 2007
}

@presentation{noauthororeditor,
  author = {leaong, Sheryl},
  interhash = {94d316680af6c91206302e964f2d7918},
  intrahash = {03ec5a6b30883646ee0c489630656b04},
  title = {A survey of recommender systems for scientific papers},
  year = 2012
}

@inproceedings{McNee:2006:DLS:1180875.1180903,
  abstract = {If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users.},
  acmid = {1180903},
  address = {New York, NY, USA},
  author = {McNee, Sean M. and Kapoor, Nishikant and Konstan, Joseph A.},
  booktitle = {Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work},
  doi = {10.1145/1180875.1180903},
  interhash = {24be686d042a3a4a710d9ff22dee0f2e},
  intrahash = {7775150ca225770019bd94db9be5db40},
  isbn = {1-59593-249-6},
  location = {Banff, Alberta, Canada},
  numpages = {10},
  pages = {171--180},
  publisher = {ACM},
  series = {CSCW '06},
  title = {Don't look stupid: avoiding pitfalls when recommending research papers},
  url = {http://doi.acm.org/10.1145/1180875.1180903},
  year = 2006
}

@article{hotho2010publikationsmanagement,
  abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{\"o}{\ss}erer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{\"a}rtigkeit, die st{\"a}ndige Verf{\"u}gbarkeit, aber auch die M{\"o}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{\"u}nde f{\"u}r ihren gegenw{\"a}rtigen Erfolg. Der Artikel f{\"u}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{\"a}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schlie{\ss}t mit Querbez{\"u}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.},
  author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and J{\"a}schke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd},
  file = {dpunkt Product page:http\://hmd.dpunkt.de/271/05.html:URL},
  interhash = {4555775b639fe1ec65a302a61ee6532c},
  intrahash = {250d83c41fb10b89c73f54bd7040bd6e},
  issn = {1436-3011},
  journal = {HMD -- Praxis der Wirtschaftsinformatik},
  month = {#feb#},
  pages = {47-58},
  title = {{Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{\"u}r Wissenschaftler}},
  volume = {Heft 271},
  year = 2010
}

@article{1742-5468-2007-06-P06010,
  abstract = {To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893 and 2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed.},
  author = {Walker, Dylan and Xie, Huafeng and Yan, Koon-Kiu and Maslov, Sergei},
  interhash = {86853f761733eaea09a273027a6c3c4a},
  intrahash = {ed618f45800255b5a5179d36849cd0b4},
  journal = {Journal of Statistical Mechanics: Theory and Experiment},
  number = 06,
  pages = {P06010},
  title = {Ranking scientific publications using a model of network traffic},
  url = {http://stacks.iop.org/1742-5468/2007/i=06/a=P06010},
  volume = 2007,
  year = 2007
}

@article{hotho2010publikationsmanagement,
  abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer gr{\"o}{\ss}erer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenw{\"a}rtigkeit, die st{\"a}ndige Verf{\"u}gbarkeit, aber auch die M{\"o}glichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gr{\"u}nde f{\"u}r ihren gegenw{\"a}rtigen Erfolg. Der Artikel f{\"u}hrt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabl{\"a}ufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schlie{\ss}t mit Querbez{\"u}gen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.},
  author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and J{\"a}schke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd},
  file = {dpunkt Product page:http\://hmd.dpunkt.de/271/05.html:URL},
  interhash = {4555775b639fe1ec65a302a61ee6532c},
  intrahash = {250d83c41fb10b89c73f54bd7040bd6e},
  issn = {1436-3011},
  journal = {HMD -- Praxis der Wirtschaftsinformatik},
  month = {#feb#},
  pages = {47-58},
  title = {{Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System f{\"u}r Wissenschaftler}},
  volume = {Heft 271},
  year = 2010
}

@article{loo2002additive,
  author = {Loo, Yueh-Lin and Willett, Robert L. and Baldwin, Kirk W. and Rogers, John A.},
  doi = {10.1063/1.1493226},
  interhash = {e701391971150fcb3c50d012aec4d91e},
  intrahash = {c7960e0ddc12ee6aa72ba3fe2d780049},
  journal = {Applied Physics Letters},
  number = 3,
  pages = {562-564},
  publisher = {AIP},
  title = {Additive, nanoscale patterning of metal films with a stamp and a surface chemistry mediated transfer process: Applications in plastic electronics},
  url = {http://link.aip.org/link/?APL/81/562/1},
  volume = 81,
  year = 2002
}

@inproceedings{conf/sigmod/WangWYY02,
  author = {Wang, Haixun and 0010, Wei Wang and Yang, Jiong and Yu, Philip S.},
  booktitle = {SIGMOD Conference},
  crossref = {conf/sigmod/2002},
  date = {2009-06-28},
  editor = {Franklin, Michael J. and Moon, Bongki and Ailamaki, Anastassia},
  ee = {http://doi.acm.org/10.1145/564691.564737},
  interhash = {9da0e61a2ac3ac371edfb251fbbfc2ae},
  intrahash = {5ad941d8f0a06bb5e570e22a8cc58d92},
  isbn = {1-58113-497-5},
  pages = {394-405},
  publisher = {ACM},
  title = {Clustering by pattern similarity in large data sets.},
  url = {http://dblp.uni-trier.de/db/conf/sigmod/sigmod2002.html#WangWYY02},
  year = 2002
}

@inproceedings{1283494,
  address = {Philadelphia, PA, USA},
  author = {Arthur, David and Vassilvitskii, Sergei},
  booktitle = {SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
  interhash = {0be633834158a3c9cba959406c3e1964},
  intrahash = {553bbfa74b13c47b4e9c7c0034a8406e},
  isbn = {978-0-898716-24-5},
  location = {New Orleans, Louisiana},
  pages = {1027--1035},
  publisher = {Society for Industrial and Applied Mathematics},
  title = {k-means++: the advantages of careful seeding},
  year = 2007
}