Editing Recommender system (section)

=== Beyond accuracy ===
Typically, research on recommender systems is concerned with finding the most accurate recommendation algorithms. However, there are a number of factors that are also important.

*'''Diversity''' – Users tend to be more satisfied with recommendations when there is a higher intra-list diversity, e.g. items from different artists.<ref name="Ziegler2005">{{cite book| vauthors=Ziegler CN, McNee SM, Konstan JA, Lausen G| chapter=Improving recommendation lists through topic diversification| title=Proceedings of the 14th international conference on World Wide Web| year=2005| pages=22–32}}</ref><ref name="castells2015">{{cite book
|last1=Castells|first1=Pablo|last2=Hurley|first2= Neil J.|last3=Vargas|first3= Saúl
|editor1-last=Ricci|editor1-first=Francesco|editor2-last=Rokach|editor2-first=Lior|editor3-last=Shapira |editor3-first=Bracha
|title=Recommender Systems Handbook|date=2015|publisher=Springer US|isbn=978-1-4899-7637-6|edition=2
|chapter=Novelty and Diversity in Recommender Systems|chapter-url = https://link.springer.com/chapter/10.1007/978-1-4899-7637-6_26|doi=10.1007/978-1-4899-7637-6_26|pages=881–918
}}</ref>

*'''Recommender persistence''' – In some situations, it is more effective to re-show recommendations,<ref name="Beel2013e">{{cite book|author1=Joeran Beel |author2=Stefan Langer |author3=Marcel Genzmehr |author4=Andreas Nürnberger | chapter=Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times| title=Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013)|date=September 2013| volume=8092| pages=390–394| publisher=Springer|editor1=Trond Aalberg |editor2=Milena Dobreva |editor3=Christos Papatheodorou |editor4=Giannis Tsakonas |editor5=Charles Farrugia | series=Lecture Notes of Computer Science (LNCS)| chapter-url=http://docear.org/papers/persistence_in_recommender_systems_--_giving_the_same_recommendations_to_the_same_users_multiple_times.pdf | access-date=1 November 2013}}</ref> or let users re-rate items,<ref name="Cosley2003">{{cite book| author1=Cosley, D.| author2= Lam, S.K.| author3= Albert, I.| author4=Konstan, J.A.| author5= Riedl, J | chapter=Is seeing believing?: how recommender system interfaces affect users' opinions| title=Proceedings of the SIGCHI conference on Human factors in computing systems| year=2003| pages=585–592| s2cid=8307833|chapter-url=https://pdfs.semanticscholar.org/d7d5/47012091d11ba0b0bf4a6630c5689789c22e.pdf}}</ref> than showing new items. There are several reasons for this. Users may ignore items when they are shown for the first time, for instance, because they had no time to inspect the recommendations carefully.
*'''Privacy''' – Recommender systems usually have to deal with privacy concerns<ref name="Pu2012">{{cite journal| author1=Pu, P.| author2=Chen, L.| author3=Hu, R.| title=Evaluating recommender systems from the user's perspective: survey of the state of the art| journal=User Modeling and User-Adapted Interaction| year=2012| pages=1–39|url=http://doc.rero.ch/record/317166/files/11257_2011_Article_9115.pdf}}</ref> because users have to reveal sensitive information. Building [[user profiles]] using collaborative filtering can be problematic from a privacy point of view. Many European countries have a strong culture of [[information privacy|data privacy]], and every attempt to introduce any level of user [[Profiling (information science)|profiling]] can result in a negative customer response. Much research has been conducted on ongoing privacy issues in this space. The [[Netflix Prize]] is particularly notable for the detailed personal information released in its dataset. Ramakrishnan et al. have conducted an extensive overview of the trade-offs between personalization and privacy and found that the combination of weak ties (an unexpected connection that provides serendipitous recommendations) and other data sources can be used to uncover identities of users in an anonymized dataset.<ref name="privacyoverview">{{cite journal |author1 = Naren Ramakrishnan |author2 = Benjamin J. Keller |author3 = Batul J. Mirza |author4 = Ananth Y. Grama |author5 = George Karypis |journal = IEEE Internet Computing |title = Privacy risks in recommender systems |year = 2001 |volume = 5 |issue = 6 |url = https://archive.org/details/sigir2002proceed0000inte/page/54 |isbn = 978-1-58113-561-9 |publisher = [[IEEE Educational Activities Department]] |location = Piscataway, NJ |pages = [https://archive.org/details/sigir2002proceed0000inte/page/54 54–62] |doi = 10.1109/4236.968832 |citeseerx = 10.1.1.2.2932 |s2cid = 1977107 }}
</ref>

*'''User demographics''' – Beel et al. found that user demographics may influence how satisfied users are with recommendations.<ref name="Beel2013f">{{cite book|author1=Joeran Beel |author2=Stefan Langer |author3=Andreas Nürnberger |author4=Marcel Genzmehr | chapter=The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems| title=Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013)|date=September 2013| pages=400–404| publisher=Springer|editor1=Trond Aalberg |editor2=Milena Dobreva |editor3=Christos Papatheodorou |editor4=Giannis Tsakonas |editor5=Charles Farrugia | chapter-url=http://docear.org/papers/the_impact_of_users'_demographics_(age_and_gender)_and_other_characteristics_on_evaluating_recommender_systems.pdf | access-date=1 November 2013}}</ref> In their paper they show that elderly users tend to be more interested in recommendations than younger users.
*'''Robustness''' – When users can participate in the recommender system, the issue of fraud must be addressed.<ref name="Konstan2012">{{cite journal| vauthors=Konstan JA, Riedl J| title=Recommender systems: from algorithms to user experience| journal=User Modeling and User-Adapted Interaction| volume=22| issue=1–2| year=2012| pages=1–23|url=https://link.springer.com/content/pdf/10.1007/s11257-011-9112-x.pdf| doi=10.1007/s11257-011-9112-x| s2cid=8996665| doi-access=free}}</ref>
*'''Serendipity''' – [[Serendipity]] is a measure of "how surprising the recommendations are".<ref name="Ricci2011">{{cite book| vauthors=Ricci F, Rokach L, Shapira B, Kantor BP| title=Recommender systems handbook| year=2011| pages=1–35| bibcode=2011rsh..book.....R}}</ref><ref name=castells2015/> For instance, a recommender system that recommends milk to a customer in a grocery store might be perfectly accurate, but it is not a good recommendation because it is an obvious item for the customer to buy. "[Serendipity] serves two purposes: First, the chance that users lose interest because the choice set is too uniform decreases. Second, these items are needed for algorithms to learn and improve themselves".<ref>{{Cite journal|last1=Möller|first1=Judith|last2=Trilling|first2=Damian|last3=Helberger|first3=Natali|last4=van Es|first4=Bram|date=2018-07-03|title=Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity|url=https://www.tandfonline.com/doi/full/10.1080/1369118X.2018.1444076|journal=Information, Communication & Society|language=en|volume=21|issue=7|pages=959–977|doi=10.1080/1369118X.2018.1444076|s2cid=149344712|issn=1369-118X|hdl=11245.1/4242e2e0-3beb-40a0-a6cb-d8947a13efb4|hdl-access=free}}</ref>
*'''Trust''' – A recommender system is of little value for a user if the user does not trust the system.<ref name="Montaner2002">{{cite book| last1=Montaner|first1= Miquel|last2= López|first2= Beatriz|last3= de la Rosa|first3= Josep Lluís| chapter=Developing trust in recommender agents| title=Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1| year=2002| pages=304–305|chapter-url=https://www.researchgate.net/publication/221454720}}</ref> Trust can be built by a recommender system by explaining how it generates recommendations, and why it recommends an item.
*'''Labelling''' – User satisfaction with recommendations may be influenced by the labeling of the recommendations.<ref name="Beel2013a">{{cite conference|vauthors=Beel, Joeran, Langer, Stefan, Genzmehr, Marcel| chapter=Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling| title=Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013)|date=September 2013| pages=395–399| chapter-url=http://docear.org/papers/sponsored_vs._organic_(research_paper)_recommendations_and_the_impact_of_labeling.pdf|editor1=Trond Aalberg |editor2=Milena Dobreva |editor3=Christos Papatheodorou |editor4=Giannis Tsakonas |editor5=Charles Farrugia | access-date=2 December 2013}}</ref> For instance, in the cited study [[click-through rate]] (CTR) for recommendations labeled as "Sponsored" were lower (CTR=5.93%) than CTR for identical recommendations labeled as "Organic" (CTR=8.86%). Recommendations with no label performed best (CTR=9.87%) in that study.