Editing Recommender system (section)

== History ==

[[Elaine Rich]] created the first recommender system in 1979, called Grundy.<ref>BEEL, Joeran, et al. Paper recommender systems: a literature survey. International Journal on Digital Libraries, 2016, 17. Jg., Nr. 4, S. 305–338.</ref><ref>RICH, Elaine. User modeling via stereotypes. Cognitive science, 1979, 3. Jg., Nr. 4, S. 329–354.</ref> She looked for a way to recommend users books they might like. Her idea was to create a system that asks users specific questions and classifies them into classes of preferences, or "stereotypes", depending on their answers. Depending on users' stereotype membership, they would then get recommendations for books they might like.

Another early recommender system, called a "digital bookshelf", was described in a 1990 technical report by [[Jussi Karlgren]] at Columbia University,
<ref>Karlgren, Jussi. "[https://jussikarlgren.wordpress.com/wp-content/uploads/1990/09/algebrawp.pdf An Algebra for Recommendations.]{{Webarchive|url=https://web.archive.org/web/20240525022319/https://jussikarlgren.wordpress.com/wp-content/uploads/1990/09/algebrawp.pdf}}.  Syslab Working Paper 179 (1990). " </ref>
and implemented at scale and worked through in technical reports and publications from 1994 onwards by [[Jussi Karlgren]], then at [[SICS]],<ref>Karlgren, Jussi. "[http://soda.swedish-ict.se/2225/2/T94_04.pdf Newsgroup Clustering Based On User Behavior-A Recommendation Algebra] {{Webarchive|url=https://web.archive.org/web/20210227090805/http://soda.swedish-ict.se/2225/2/T94_04.pdf |date=February 27, 2021 }}." SICS Research Report (1994).</ref><ref>{{cite web|last1=Karlgren|first1=Jussi|title=A digital bookshelf: original work on recommender systems|url=https://jussikarlgren.wordpress.com/2017/10/01/a-digital-bookshelf-original-work-on-recommender-systems/|access-date=27 October 2017|date=October 2017}}</ref>
and research groups led by [[Pattie Maes]] at MIT,<ref>
Shardanand, Upendra, and Pattie Maes. "[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.30.6583&rep=rep1&type=pdf Social information filtering: algorithms for automating "word of mouth"]." In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co., 1995.
</ref> Will Hill at Bellcore,<ref>Hill, Will, Larry Stead, Mark Rosenstein, and George Furnas. "[http://zhang.ist.psu.edu/teaching/501/readings/Hill.pdf Recommending and evaluating choices in a virtual community of use] {{Webarchive|url=https://web.archive.org/web/20181221074205/http://zhang.ist.psu.edu/teaching/501/readings/Hill.pdf |date=2018-12-21 }}." In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 194–201. ACM Press/Addison-Wesley Publishing Co., 1995.</ref> and [[Paul Resnick]], also at MIT,<ref>Resnick, Paul, Neophytos Iacovou, Mitesh Suchak, Peter Bergström, and John Riedl. "[https://sites.ualberta.ca/~golmoham/SW/web%20mining%2023Jan2008/GroupLens%20An%20Open%20Architecture%20for%20Collaborating%20Filtering%20%20of%20Netnews.pdf GroupLens: an open architecture for collaborative filtering of netnews]." In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. ACM, 1994.</ref><ref name="ResnickVarian">Resnick, Paul, and Hal R. Varian. "Recommender systems." Communications of the ACM 40, no. 3 (1997): 56–58.</ref> whose work with GroupLens was awarded the 2010 [[ACM Software Systems Award]].

Montaner provided the first overview of recommender systems from an intelligent agent perspective.<ref>{{Cite journal |last1 = Montaner |first1 = M. |last2 = Lopez |last3 = de la Rosa |first3 = J. L. |title = A Taxonomy of Recommender Agents on the Internet |journal = Artificial Intelligence Review |volume = 19 |issue = 4 |date = June 2003 |pages = 285–330 |doi = 10.1023/A:1022850703159 |first2 = B. |s2cid = 16544257 }}.</ref> [[Gediminas Adomavicius|Adomavicius]] provided a new, alternate overview of recommender systems.<ref name="Toward the Next Generation of Recommender Systems">{{Cite journal |last1 = Adomavicius |first1 = G. |last2 = Tuzhilin |first2 = A.|author2-link= Alexander Tuzhilin |url = http://portal.acm.org/citation.cfm?id=1070611.1070751 |title = Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions |journal = IEEE Transactions on Knowledge and Data Engineering |volume = 17 |issue = 6 |date = June 2005 |pages = 734–749 |doi = 10.1109/TKDE.2005.99 |citeseerx = 10.1.1.107.2790 |s2cid = 206742345 }}.</ref>  Herlocker provides an additional overview of evaluation techniques for recommender systems,<ref>{{Cite journal |last1 = Herlocker |first1 = J. L. |last2 = Konstan |first2 = J. A. |last3 = Terveen |first3 = L. G. |last4 = Riedl |first4 = J. T. |date = January 2004 |title = Evaluating collaborative filtering recommender systems |journal = ACM Trans. Inf. Syst. |volume = 22 |issue = 1 |pages = 5–53 |doi = 10.1145/963770.963772 |citeseerx = 10.1.1.78.8384 |s2cid = 207731647 }}.</ref> and [[Joeran Beel|Beel]] et al. discussed the problems of offline evaluations.<ref name=":0">{{Cite book |last1 = Beel |first1 = J. |last2 = Genzmehr |first2 = M. |last3 = Gipp |first3 = B. |title = Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation |chapter = A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation |date = October 2013 |chapter-url = http://docear.org/papers/a_comparative_analysis_of_offline_and_online_evaluations_and_discussion_of_research_paper_recommender_system_evaluation.pdf |pages = 7–14 |doi = 10.1145/2532508.2532511 |isbn = 978-1-4503-2465-6 |s2cid = 8202591 |access-date = 2013-10-22 |archive-date = 2016-04-17 |archive-url = https://web.archive.org/web/20160417222305/http://docear.org/papers/a_comparative_analysis_of_offline_and_online_evaluations_and_discussion_of_research_paper_recommender_system_evaluation.pdf  }}</ref> Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges.<ref>{{Cite book |last1 = Beel|first1 = J. |last2 = Langer |first2 = S. |last3 = Genzmehr |first3 = M. |last4 = Gipp |first4 = B.  |last5 = Breitinger |first5 = C. |title = Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation |chapter = Research paper recommender system evaluation: A quantitative literature survey |date = October 2013 |pages = 15–22 |chapter-url = http://docear.org/papers/research_paper_recommender_system_evaluation--a_quantitative_literature_survey.pdf |doi = 10.1145/2532508.2532512 |isbn = 978-1-4503-2465-6 |s2cid = 4411601 |url = http://nbn-resolving.de/urn:nbn:de:bsz:352-0-285593 }}</ref><ref>{{Cite journal |last1 = Beel |first1 = J. |last2 = Gipp|first2 = B. |last3 = Langer |first3 = S. |last4 = Breitinger |first4 = C. | date = 26 July 2015  |pages = 305–338 |doi = 10.1007/s00799-015-0156-0 |title = Research Paper Recommender Systems: A Literature Survey |journal = International Journal on Digital Libraries |volume = 17 |issue = 4 |s2cid = 207035184 |url = http://nbn-resolving.de/urn:nbn:de:bsz:352-0-311312 }}</ref>