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Multi-agent recommender system

conference contribution
posted on 22.07.2021, 10:07 by Abdullah AlhejailiAbdullah Alhejaili, Syeda FatimaSyeda Fatima
A recommender agent (RA) provides users with recommendations about products/ services. Recommendations are made on the basis of information available about the products/ services and the users, and this process typically involves making predictions about user preferences and matching them with product attributes. Machine learning methods are being studied extensively to design RAs. In this approach, a model is learnt from historical data about trading (i.e. data about products and the users buying them). There are numerous different learning methods, and how accurately a method can make a recommendation depends on the method and also on the type of historical data. Given this, we propose a multi-agent recommender system called MARS which combines various different machine learning methods. Within MARS, different agents are designed to make recommendations using different machine learning methods. Since different agents use different machine learning methods, the recommendations they make may be conflicting. Negotiation is used to come to an agreement on a recommendation. Negotiation is conducted using a contract-net protocol. The performance of MARS is evaluated in terms of recommendation error. The results of simulations show that MARS outperforms five existing recommender systems.

History

School

  • Science

Department

  • Computer Science

Published in

Recent Advances in Agent-based Negotiation Formal Models and Human Aspects

Pages

103 - 119

Source

12th International Workshop on Automated Negotiations (ACAN) held in Macao, 2019, in conjunction with International Joint Conference on Artificial Intelligence (IJCAI) 2019

Publisher

Springer

Version

AM (Accepted Manuscript)

Rights holder

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature

Publisher statement

This is a pre-copyedited version of a contribution published in Recent Advances in Agent-based Negotiation Formal Models and Human Aspects edited by Reyhan Aydoğan ..et al. published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-981-16-0471-3_7

Publication date

2021-05-11

Copyright date

2021

ISBN

9789811604737; 9789811604737

ISSN

1860-949X

eISSN

1860-9503

Book series

Studies in Computational Intelligence book series (SCI, volume 958)

Language

en

Editor(s)

Reyhan Aydoğan; Takayuki Ito; Ahmed Moustafa; Takanobu Otsuka; Minjie Zhang

Location

Macao, China

Event dates

10th August 2019 - 16th August 2019

Depositor

Dr Syeda Fatima . Deposit date: 21 July 2021

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