Improving recommendations through an assumption-based multiagent approach: An application in the tourism domain

Authors:
Fabiana Lorenzi;Ana L. C. Bazzan;Mara Abel;Francesco Ricci
Affiliations:
PPGC-Instituto de Informática, UFRGS, Caixa Postal 15064, 91.501-970 Porto Alegre, RS, Brazil and Universidade Luterana do Brasil, Av. Farroupilha, 8001 Canoas, RS, Brazil;PPGC-Instituto de Informática, UFRGS, Caixa Postal 15064, 91.501-970 Porto Alegre, RS, Brazil;PPGC-Instituto de Informática, UFRGS, Caixa Postal 15064, 91.501-970 Porto Alegre, RS, Brazil;Free University of Bozen-Bolzano, Italy
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 9
Cited 1

An assumption-based TMS

Artificial Intelligence
DATMS: a framework for distributed assumption based reasoning

Distributed artificial intelligence: vol. 2
GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
SmartClients: Constraint Satisfaction as a Paradigm for Scaleable Intelligent Information Systems

Constraints
Recommender systems: a market-based design

AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Architectural design of a multi-agent recommender system for the legal domain

Proceedings of the 11th international conference on Artificial intelligence and law
User evaluation of a market-based recommender system

Autonomous Agents and Multi-Agent Systems
A multi agent recommender system that utilises consumer reviews in its recommendations

International Journal of Intelligent Information and Database Systems

Profile recommendation in communities of practice based on multiagent systems

SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	12.05

Visualization

Abstract

Recommender systems are popular tools dealing with the information overload problem in e-commerce web sites. The more they know about the users, the better recommendations they can provide. However, sometimes, in real situations, it is necessary to make guesses about the value of missing but useful data in order to generate a recommendation immediately, rather than waiting the data becomes available. This paper presents an assumption-based multiagent recommender system capable of making these types of assumptions about the preferences of the users. The approach was validate in the tourism domain (recommendation of travel packages). Experiments were conducted to illustrate the impact of various assumption making strategies on the quality of the recommendations as well as the impact of trust assignment.