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.
%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 = {2008-10-26T18:24:09.000+0100},
author = {Perugini, Saverio and Gonçalves, Marcos André and Fox, Edward A.},
biburl = {https://www.bibsonomy.org/bibtex/2d429c4aeb604ecb53c6de7c5495976cd/fl%c3%b6ckchen},
interhash = {9471d56513f55cfc8574751baa8cb1fb},
intrahash = {d429c4aeb604ecb53c6de7c5495976cd},
journal = {Journal of Intelligent Information Systems},
keywords = {Information_Retrieval Recommendersysteme wismasys0809},
month = {September},
number = 2,
pages = {107-143},
timestamp = {2008-11-09T20:51:36.000+0100},
title = {Recommender systems research: A connection-centric study.},
url = {http://www.springerlink.com/content/r322040289831534/},
volume = 23,
year = 2004
}