Evaluation of P2P information recommendation based on collaborative filtering

Authors:
Hidehiko Okada;Makoto Inoue
Affiliations:
Kyoto Sangyo University, Kamigamo Motoyama, Kita-ku, Kyoto, Japan;Kyoto Sangyo University, Kamigamo Motoyama, Kita-ku, Kyoto, Japan
Venue:
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Year:
2007

Citing 8
Cited 0

Using collaborative filtering to weave an information tapestry

Communications of the ACM - Special issue on information filtering
Recommender systems

Communications of the ACM
Introduction: personalized views of personalization

Communications of the ACM
Peer-to-Peer: Harnessing the Power of Disruptive Technologies

Peer-to-Peer: Harnessing the Power of Disruptive Technologies
Introduction

Communications of the ACM
Introduction to recommender systems: Algorithms and Evaluation

ACM Transactions on Information Systems (TOIS)
Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems (TOIS)
A survey of peer-to-peer content distribution technologies

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Collaborative filtering is a social information recommendation/ filtering method, and the peer-to-peer (P2P) computer network is a network on which information is distributed on the peer-to-peer basis (each peer node works as a server, a client, and even a router). This research aims to develop a model of P2P information recommendation system based on collaborative filtering and evaluate the ability of the system by computer simulations based on the model. We previously proposed a simple model, and the model in this paper is a modified one that is more focused on recommendation agents and user-agent interactions. We have developed a computer simulator program and tested simulations with several parameter settings. From the results of the simulations, recommendation recall and precision are evaluated. Findings are that the agents are likely to overly recommend so that the recall score becomes high but the precision score becomes low.