Modeling recommendation as a social choice problem

Authors:
Massimiliano Albanese;Antonio d'Acierno;Vincenzo Moscato;Fabio Persia;Antonio Picariello
Affiliations:
University of Maryland, College Park, MD, USA;ISA-CNR, Avellino, Italy;University of Naples, Naples, Italy;University of Naples, Naples, Italy;University of Naples, Naples, Italy
Venue:
Proceedings of the fourth ACM conference on Recommender systems
Year:
2010

Citing 6
Cited 2

Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Reputation systems

Communications of the ACM
Trust-based recommendation systems: an axiomatic approach

Proceedings of the 17th international conference on World Wide Web
Axiomatic foundations for ranking systems

Journal of Artificial Intelligence Research
Content-based recommendation systems

The adaptive web
Hybrid web recommender systems

The adaptive web

SNOPS: a smart environment for cultural heritage applications

Proceedings of the twelfth international workshop on Web information and data management
A Novel Strategy for Recommending Multimedia Objects and its Application in the Cultural Heritage Domain

International Journal of Multimedia Data Engineering & Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the classical theory of social choice, a set of voters is called to rank a set of alternatives and a social ranking of the alternatives is generated. In this paper, we model recommendation in the context of browsing systems as a social choice problem, where the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We then propose an importance ranking method that strongly resembles the well known PageRank ranking system, and takes into account both the browsing behavior of the users and the intrinsic features of the objects in the collection. We apply the proposed approach in the context of multimedia browsing systems and show that it can generate effective recommendations and can scale well for large data collections.