%0 Journal Article %1 Perugini-Recommender-2004 %A Perugini, Saverio %A Gonçalves, Marcos André %A Fox, Edward A. %D 2004 %J Journal of Intelligent Information Systems %K Information_Retrieval Recommendersysteme wismasys0809 %N 2 %P 107-143 %T Recommender systems research: A connection-centric study. %U http://www.springerlink.com/content/r322040289831534/ %V 23 %X Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.

@article{Perugini-Recommender-2004, abstract = {Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address.}, added-at = {2008-11-11T19:07:17.000+0100}, author = {Perugini, Saverio and Gonçalves, Marcos André and Fox, Edward A.}, biburl = {https://www.bibsonomy.org/bibtex/2d429c4aeb604ecb53c6de7c5495976cd/suny}, interhash = {9471d56513f55cfc8574751baa8cb1fb}, intrahash = {d429c4aeb604ecb53c6de7c5495976cd}, journal = {Journal of Intelligent Information Systems}, keywords = {Information_Retrieval Recommendersysteme wismasys0809}, month = {September}, number = 2, pages = {107-143}, timestamp = {2008-11-11T19:07:17.000+0100}, title = {Recommender systems research: A connection-centric study.}, url = {http://www.springerlink.com/content/r322040289831534/}, volume = 23, year = 2004 }