Recommender Systems Research: A Connection-Centric Survey

Saverio Perugini, Marcos André Gonçalves, Edward A. Fox
2004 Journal of Intelligent Information Systems  
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
more » ... ed 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 underemphasized 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. "What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it." Herbert A. Simon Social Network connections p2 p3 p4 p5 p1 keywords, surveys, feedback reviews, formation User Modeling Explicit discovery Implicit DB data user−generated the Web (e.g., ratings of user representation composite or profile) documents, communication logs Usenet msgs Figure 1. A connection-centric view of recommendation as bringing people together into a social network (center). (left) Formation of a social network by explicitly collecting ratings or profiles. (right) Identification and discovery of a network by exposing self-organizing communities implicit in user-generated data such as communication or web logs. Although not illustrated explicitly, these two approaches may be combined.
doi:10.1023/b:jiis.0000039532.05533.99 fatcat:rscxs4oypfffpkvkbntzz4xnx4