http://www.bibsonomy.org/burst/concept/tag/recommenderBibSonomy BuRST Feed for /concept/tag/recommender2008-09-05T09:18:34+02:00My Bibliographyhttp://www.bibsonomy.org/bibtex/2b27a8dbb9b48dca268d14a7272c01154/jamiebullockjamiebullock2008-08-22T12:47:06+02:00systems} {self, recommender <span style="color:#555555;">David <a href="http://www.bibsonomy.org/author/Gleich">Gleich</a> und Leonid <a href="http://www.bibsonomy.org/author/Zhukov">Zhukov</a> und Matthew <a href="http://www.bibsonomy.org/author/Rasmussen">Rasmussen</a> und Kevin <a href="http://www.bibsonomy.org/author/Lang">Lang</a> </span><em>Information Visualization 2005, </em>(<em>2005</em>) <em>Interactive Poster . </em>Fri Aug 22 12:47:06 CEST 2008{Information Visualization 2005}{Interactive Poster}{The World of Music: SDP Embedding of High Dimensional data}{2005}systems} {self, recommender dblphttp://www.bibsonomy.org/bibtex/26010e52c35a0bbb74d23771bbaeabaa3/skaseyskasey2008-08-18T15:37:50+02:00recommender tourism <span style="color:#555555;">Victoria <a href="http://www.bibsonomy.org/author/Bellotti">Bellotti</a> und James Bo <a href="http://www.bibsonomy.org/author/Begole">Begole</a> und Ed H. <a href="http://www.bibsonomy.org/author/Chi">Chi</a> und Nicolas <a href="http://www.bibsonomy.org/author/Ducheneaut">Ducheneaut</a> und Ji <a href="http://www.bibsonomy.org/author/Fang">Fang</a> und Ellen <a href="http://www.bibsonomy.org/author/Isaacs">Isaacs</a> und Tracy H. <a href="http://www.bibsonomy.org/author/King">King</a> und Mark W. <a href="http://www.bibsonomy.org/author/Newman">Newman</a> und Kurt <a href="http://www.bibsonomy.org/author/Partridge">Partridge</a> und Bob <a href="http://www.bibsonomy.org/author/Price">Price</a> und Paul <a href="http://www.bibsonomy.org/author/Rasmussen">Rasmussen</a> und Michael <a href="http://www.bibsonomy.org/author/Roberts">Roberts</a> und Diane J. <a href="http://www.bibsonomy.org/author/Schiano">Schiano</a> und Alan <a href="http://www.bibsonomy.org/author/Walendowski">Walendowski</a> </span><em>CHI, </em><em>Seite1157-1166. </em><em>ACM, </em>(<em>2008</em>)Mon Aug 18 15:37:50 CEST 2008CHIconf/chi/20081157-1166Activity-based serendipitous recommendations with the Magitti mobile leisure guide.2008recommender tourism http://www.bibsonomy.org/bibtex/2a2f608b6612797c29020cf75e9daa75f/skaseyskasey2008-08-18T15:36:00+02:00recommender social <span style="color:#555555;">Philip <a href="http://www.bibsonomy.org/author/Bonhard">Bonhard</a> und Clare <a href="http://www.bibsonomy.org/author/Harries">Harries</a> und John <a href="http://www.bibsonomy.org/author/Mccarthy">Mccarthy</a> und Angela M. <a href="http://www.bibsonomy.org/author/Sasse">Sasse</a> </span><em>CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems, </em><em>Seite1057--1066. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2006</em>)Mon Aug 18 15:36:00 CEST 2008New York, NY, USACHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems1057--1066Accounting for taste: using profile similarity to improve recommender systems2006recommender social huge bibliographyhttp://www.bibsonomy.org/bibtex/2552fdc78cf0ed8086d1c54436c19931e/rwdairwdai2008-08-18T13:59:15+02:00colfil recommender overview <span style="color:#555555;">Gediminas <a href="http://www.bibsonomy.org/author/Adomavicius">Adomavicius</a> und Alexander <a href="http://www.bibsonomy.org/author/Tuzhilin">Tuzhilin</a> </span><em>IEEE Trans. Knowl. Data Eng.</em><em>17(6):734-749</em>(<em>2005</em>)Mon Aug 18 13:59:15 CEST 2008IEEE Trans. Knowl. Data Eng.6734-749Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions.172005colfil recommender overview http://www.bibsonomy.org/bibtex/2157846898c1c2a65c265a913ebac115a/michaelmichael2008-08-13T11:00:11+02:00tagging projekt ws07 kde content classification recommender tagging_proposal seminar tagging_convergence bookmarking <span style="color:#555555;">Andrew <a href="http://www.bibsonomy.org/author/Byde">Byde</a> und Hui <a href="http://www.bibsonomy.org/author/Wan">Wan</a> und Steve <a href="http://www.bibsonomy.org/author/Cayzer">Cayzer</a> </span><em>Proceedings of the International Conference on Weblogs and Social Media, </em><em>March2007. </em>Wed Aug 13 11:00:11 CEST 2008Proceedings of the International Conference on Weblogs and Social MediaMarchPersonalized Tag Recommendations via Tagging and Content-based Similarity Metrics2007tagging projekt ws07 kde content classification recommender tagging_proposal seminar tagging_convergence bookmarking This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.Integrated personal recommender systemshttp://www.bibsonomy.org/bibtex/241556cf9aec7bab2779a82c30b3a44c2/mschubermschuber2008-07-08T09:33:45+02:00system to-read recommender <span style="color:#555555;">Ronald <a href="http://www.bibsonomy.org/author/Chung">Chung</a> und David <a href="http://www.bibsonomy.org/author/Sundaram">Sundaram</a> und Ananth <a href="http://www.bibsonomy.org/author/Srinivasan">Srinivasan</a> </span><em>ICEC '07: Proceedings of the ninth international conference on Electronic commerce, </em><em>Seite65--74. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Tue Jul 08 09:33:45 CEST 2008New York, NY, USAICEC '07: Proceedings of the ninth international conference on Electronic commerce65--74Integrated personal recommender systems2007system to-read recommender http://www.bibsonomy.org/bibtex/22c3aea62cafdd8a01a68e3fbed7b4071/michimichi2008-06-18T14:57:01+02:00learning classification supervised kde projekt recommender <span style="color:#555555;">Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> </span><em>Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005, </em><em>Seite378-385. </em><em>Houston, Texas, USA, </em><em>IEEE Computer Society, </em>(<em>2005</em>)Wed Jun 18 14:57:01 CEST 2008Houston, Texas, USAProceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005378-385Compound Classification Models for Recommender Systems.2005learning classification supervised kde projekt recommender DBLP Record 'conf/pkdd/JaschkeMHSS07'http://www.bibsonomy.org/bibtex/2b8b87c78e9e27a44aacde0402c642bff/michimichi2008-06-18T14:56:57+02:00recommender recommendations tags bibsonomy tagging folksonomies <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Leandro Balby <a href="http://www.bibsonomy.org/author/Marinho">Marinho</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings, </em><em>Volume4702vonLecture Notes in Computer Science, </em><em>Seite506-514. </em><em>Springer, </em>(<em>2007</em>)Wed Jun 18 14:56:57 CEST 2008Knowledge Discovery in Databases: PKDD 2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, ProceedingsDBLP:conf/pkdd/2007506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies47022007recommender recommendations tags bibsonomy tagging folksonomies Imported BibTeXhttp://www.bibsonomy.org/bibtex/2480bffe0af7ec4e5b910e9107a2c464b/ocelmaocelma2008-05-30T07:04:55+02:00path coefficient electronic filter curve collaborative longtail lorenz recommender simulation concentration diversity commerce dependence sales <span style="color:#555555;">Daniel M. <a href="http://www.bibsonomy.org/author/Fleder">Fleder</a> und Kartik <a href="http://www.bibsonomy.org/author/Hosanagar">Hosanagar</a> </span><em>SSRN eLibrary</em>(<em>2007</em>)Fri May 30 07:04:55 CEST 2008SSRN eLibrary{Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity}Working Paper Series2007path coefficient electronic filter curve collaborative longtail lorenz recommender simulation concentration diversity commerce dependence sales Imported BibTeXhttp://www.bibsonomy.org/bibtex/2dfb15f8c755cb23f4fa3b6b58c0d6310/ocelmaocelma2008-05-30T07:04:55+02:00recommender <span style="color:#555555;">Sean M. <a href="http://www.bibsonomy.org/author/Mcnee">Mcnee</a> und John <a href="http://www.bibsonomy.org/author/Riedl">Riedl</a> und Joseph A. <a href="http://www.bibsonomy.org/author/Konstan">Konstan</a> </span><em>Computer Human Interaction. Human factors in computing systems, </em><em>Seite1097-1101. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2006</em>)Fri May 30 07:04:55 CEST 2008New York, NY, USAComputer Human Interaction. Human factors in computing systems1097-1101Being accurate is not enough: how accuracy metrics have hurt recommender systems2006recommender Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissionshttp://www.bibsonomy.org/bibtex/23a7b2b52257c04b0da0702a820c3f0fc/hothohotho2008-05-29T17:31:25+02:00recommender folksonomy <span style="color:#555555;">Satoshi <a href="http://www.bibsonomy.org/author/Niwa">Niwa</a> und Takuo <a href="http://www.bibsonomy.org/author/Doi">Doi</a> und Shinichi <a href="http://www.bibsonomy.org/author/Honiden">Honiden</a> </span><em>itng</em>(<em>2006</em>)Thu May 29 17:31:25 CEST 2008Los Alamitos, CA, USAitng388-393Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions002006recommender folksonomy http://www.bibsonomy.org/bibtex/2ed541c5cc2c2200b98ea7c46637389ac/mgranimgrani2008-05-27T08:15:25+02:00ausleihen recommender tagging folksonomy <span style="color:#555555;">Robert <a href="http://www.bibsonomy.org/author/J\&#034;aschke">J&#228;schke</a> und Leandro Balby <a href="http://www.bibsonomy.org/author/Marinho">Marinho</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Lars <a href="http://www.bibsonomy.org/author/Schmidt-Thieme">Schmidt-Thieme</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Knowledge Discovery in Databases: PKDD 2007, </em><em>Volume4702vonLecture Notes in Computer Science, </em><em>Seite506-514. </em><em>Berlin, Heidelberg, </em><em>Springer, </em>(<em>2007</em>)Tue May 27 08:15:25 CEST 2008Berlin, HeidelbergKnowledge Discovery in Databases: PKDD 2007506-514Lecture Notes in Computer ScienceTag Recommendations in Folksonomies47022007ausleihen recommender tagging folksonomy Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare two recommendation algorithms on largescale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.Query expansion using associated querieshttp://www.bibsonomy.org/bibtex/2f172777486a31a8f01522702b9f8eef3/mstrohmmstrohm2008-04-23T11:21:19+02:00recommender MUSTREAD query-log-analysis <span style="color:#555555;">B. <a href="http://www.bibsonomy.org/author/Billerbeck">Billerbeck</a> und F. <a href="http://www.bibsonomy.org/author/Scholer">Scholer</a> und H. E. <a href="http://www.bibsonomy.org/author/Williams">Williams</a> und J. <a href="http://www.bibsonomy.org/author/Zobel">Zobel</a> </span><em>CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management, </em><em>Seite2--9. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2003</em>)Wed Apr 23 11:21:19 CEST 2008New York, NY, USACIKM '03: Proceedings of the twelfth international conference on Information and knowledge management2--9Query expansion using associated queries2003recommender MUSTREAD query-log-analysis Hundreds of millions of users each day use web search engines to meet their information needs. Advances in web search effectiveness are therefore perhaps the most significant public outcomes of IR research. Query expansion is one such method for improving the effectiveness of ranked retrieval by adding additional terms to a query. In previous approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We propose a new method of obtaining expansion terms, based on selecting terms from past user queries that are associated with documents in the collection. Our scheme is effective for query expansion for web retrieval: our results show relative improvements over unexpanded full text retrieval of 26%--29%, and 18%--20% over an optimised, conventional expansion approach.http://www.bibsonomy.org/bibtex/2cbd22faf83cf4d4148d44f222adbda0d/mstrohmmstrohm2008-04-23T11:21:02+02:00query-log-analysis TOREAD recommender <span style="color:#555555;">B. M. <a href="http://www.bibsonomy.org/author/Fonseca">Fonseca</a> und P. <a href="http://www.bibsonomy.org/author/Golgher">Golgher</a> und B. <a href="http://www.bibsonomy.org/author/P\^{o}ssas">P^ossas</a> und B. <a href="http://www.bibsonomy.org/author/Ribeiro-Neto">Ribeiro-Neto</a> und N. <a href="http://www.bibsonomy.org/author/Ziviani">Ziviani</a> </span><em>CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management, </em><em>Seite696--703. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2005</em>)Wed Apr 23 11:21:02 CEST 2008New York, NY, USACIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management696--703Concept-based interactive query expansion2005query-log-analysis TOREAD recommender Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for Web ranking algorithms. In fact, there are still many situations in which the users are presented with imprecise or very poor results. One of the key difficulties is the fact that users usually submit very short and ambiguous queries, and they do not fully specify their information needs. That is, it is necessary to improve the query formation process if better answers are to be provided. In this work we propose a novel concept-based query expansion technique, which allows disambiguating queries submitted to search engines. The concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from query relations mined using association rules. The concepts related to the current query are then shown to the user who selects the one concept that he interprets is most related to his query. This concept is used to expand the original query and the expanded query is processed instead. Using a Web test collection, we show that our approach leads to gains in average precision figures of roughly 32%. Further, if the user also provides information on the type of relation between his query and the selected concept, the gains in average precision go up to roughly 52%.Searching for experts on the Webhttp://www.bibsonomy.org/bibtex/26a1ef565dcdedd5fb43677b5988caeac/bluedolphinbluedolphin2008-04-18T17:51:11+02:00expertise article knowledge study recommender expert_finder <span style="color:#555555;">Irma <a href="http://www.bibsonomy.org/author/Becerra-Fernandez">Becerra-Fernandez</a> </span><em>ACM Transactions on Internet Technologies</em><em>6(4):333--355</em>(<em>2006</em>)Fri Apr 18 17:51:11 CEST 2008New York, NY, USAACM Transactions on Internet Technologies4333--355Searching for experts on the Web: A review of contemporary expertise locator systems62006expertise article knowledge study recommender expert_finder This article presents the role of ontologies and Web mining techniques in the construction and maintenance of experts' profiles. This article also discusses the development of contemporary expertise-locator knowledge management systems and, specifically, the implementation details of two such systems: the Searchable Answer Generating Engine (SAGE), and Expert Seeker. SAGE is a Web-based expertise locator system, which searches for researchers in universities in Florida based on specified criteria, including expertise in a specific domain. Expert Seeker's purpose is to search for experts in one of the best-known knowledge organizations, the National Aeronautics and Space Administration. Implementation details, results to date, and future plans for these systems are also presented.Dissertationhttp://www.bibsonomy.org/bibtex/2880e27782ec86737a931b9d92d9e905a/minguezminguez2008-04-03T13:26:44+02:00matchmaking master recommender <span style="color:#555555;">G. <a href="http://www.bibsonomy.org/author/Linden">Linden</a> und B. <a href="http://www.bibsonomy.org/author/Smith">Smith</a> und J. <a href="http://www.bibsonomy.org/author/York">York</a> </span><em>Internet Computing, IEEE</em><em>7(1):76--80</em>(<em>2003</em>)Thu Apr 03 13:26:44 CEST 2008Internet Computing, IEEE176--80Amazon.com recommendations: item-to-item collaborative filtering72003matchmaking master recommender Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.Analysis of recommendation algorithms for e-commercehttp://www.bibsonomy.org/bibtex/28761b337bd08a4ac7b72bac4c84851fb/minguezminguez2008-04-03T13:20:29+02:00matchmaking recommender master <span style="color:#555555;">Badrul <a href="http://www.bibsonomy.org/author/Sarwar">Sarwar</a> und George <a href="http://www.bibsonomy.org/author/Karypis">Karypis</a> und Joseph <a href="http://www.bibsonomy.org/author/Konstan">Konstan</a> und John <a href="http://www.bibsonomy.org/author/Riedl">Riedl</a> </span><em>EC '00: Proceedings of the 2nd ACM conference on Electronic commerce, </em><em>Seite158--167. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2000</em>)Thu Apr 03 13:20:29 CEST 2008New York, NY, USAEC '00: Proceedings of the 2nd ACM conference on Electronic commerce158--167Analysis of recommendation algorithms for e-commerce2000matchmaking recommender master http://www.bibsonomy.org/bibtex/21b86a08b94f10c27d35c4f2ac00c66e0/krasskrass2008-04-01T13:03:42+02:00systems recommender <span style="color:#555555;">Josep Lluis De La Rosa Miguel <a href="http://www.bibsonomy.org/author/Montaner">Montaner</a> </span><em>Artificial Intelligence Review</em>(<em>2003</em>)Tue Apr 01 13:03:42 CEST 2008Artificial Intelligence Review285-330A Taxonomy of Recommender Agents on the Internet192003systems recommender Tag Boosterhttp://www.bibsonomy.org/bibtex/21af9877eebe38cdedc6578967ae76d8a/mginfmginf2008-03-27T17:50:26+01:00ranking tag microsoft tagging suggestion recommender <span style="color:#555555;">M. <a href="http://www.bibsonomy.org/author/Vojnovic">Vojnovic</a> und J. <a href="http://www.bibsonomy.org/author/Cruise">Cruise</a> und D. <a href="http://www.bibsonomy.org/author/Gunawardena">Gunawardena</a> und P. <a href="http://www.bibsonomy.org/author/Marbach">Marbach</a> </span>(<em>2007</em>)Thu Mar 27 17:50:26 CET 2008MSR-TR-2007-06Ranking and Suggesting Tags in Collaborative Tagging Applications2007ranking tag microsoft tagging suggestion recommender Recommendersysteme - Informationswissenschaft -> citavi, OK.http://www.bibsonomy.org/bibtex/2501849ade58a25831e72519f6add1313/ewomantewomant2008-03-14T14:33:12+01:00recommender folksonomy information_retrieval <span style="color:#555555;">Stefanie <a href="http://www.bibsonomy.org/author/Höhfeld">H&#246;hfeld</a> und Melanie <a href="http://www.bibsonomy.org/author/Kwiatkowski">Kwiatkowski</a> </span><em>IWP-Information Wissenschaft &amp; Praxis</em><em>58(5):265-276</em>(<em>2007</em>)Fri Mar 14 14:33:12 CET 2008IWP-Information Wissenschaft & Praxis5265-276Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art582007recommender folksonomy information_retrieval Empfehlungssysteme tragen Inhalte individuell an Nutzer im WWW heran, basierend auf deren konkreten Bedürfnissen, Vorlieben und Interessen. Solche Systeme können Produkte, Services, Nutzer (mit analogen Interessen) uvm. vorschlagen und stellen damit – gerade im Web 2.0-Zeitalter – eine besondere Form der Personalisierung sowie des social networking dar. Damit bieten Empfehlungssysteme Anbietern im ECommerce einen entscheidenden Marktvorteil, weshalb die Auswertung der Kundendaten bei großen Firmen wie Amazon, Google oder Ebay eine hohe Priorität besitzt. Aus diesem Grund wird im vorliegenden Artikel auf die Ansätze von Empfehlungssystemen, welche auf unterschiedliche Weise die Bedürfnisse des Nutzers aufgreifen bzw. „vorausahnen“ und ihm Vorschläge (aus verschiedenen Bereichen) unterbreiten können, eingegangen. Der Artikel liefert eine Definition und Darstellung der Arbeitsweisen von Empfehlungssystemen. Dabei werden die verschiedenen Methodiken jener Dienste vergleichend erläutert, um ihre jeweiligen Vor- und Nachteile deutlich zu machen. Außerdem wird der Ontologie- und Folksonomy-Einsatz innerhalb von Empfehlungssystemen beleuchtet, um Chancen und Risiken der Anwendung von Methoden der Wissensrepräsentation für zukünftige Forschungsarbeiten einschätzen zu können. Recommender Systems in an Information Science View – The State of the Art Recommender systems offer content individually to users in the WWW, based on their concrete needs, preferences and interests. Those systems can propose products, services, users (with analogous interests), etc.) and represent a special form of personalisation as well as of social networking – exactly in the Web 2.0 age. Recommender systems offer e.g. suppliers in the e-commerce a crucial market advantage. So, the evaluation of the customer data has high priority at big companies like Amazon, Google or Ebay. For this reason we engaged in recommender systems, which take up the user’s needs in different ways, to “anticipate“ needs and make suggestions (from different areas) to the user. This review article achieves a definition and representation of operations and methods of recommender systems. Exactly the different methodologies of those services should be expounded comparativly on that occasion in order to represent advantages and disadvantages. The use of ontologies and folksonomies as implementations in recommender systems is portrayed in order to be able to take into consideration chances and risks of the application of knowledge representation methods for future researches.