mk:incos11c

Summary

Recommendation System Based on Competing Algorithms. Elnaz Mazandarani, Kaori Yoshida, Mario Köppen and Wladimir Bodrow. In Proc. Third International Conference on Intelligent Networking and Collaborative Systems (INCoS 2011), pages 857-862, Fukuoka, Japan, November 2011.

Abstract

Abstract---In this paper, we provide the idea of analyzing the performance of algorithms generating personal recommendation by using competing algorithms for one and the same recommendation request based on same situation and information in a unified framework. The analysis of the recently proposed collaborative filter PAF (Popularity Among Friends) for finding user similarity based on past ratings and evaluation of missing ratings of a user-item-matrix in order to generate recommendations will serve as a base to specify competing algorithm for an experimental recommendation system. We present results of an on-line experiment of the proposed recommendation system which will demonstrate the advantage of directly comparing the rate of user acceptance of competing algorithms and allow a statement about their suitability as base of an easy evaluation of the system. The evaluation gives a conclusion about algorithms to be replaced through new competing methods in order to steadily improve the recommendation system.

Bibtex entry

@INPROCEEDINGS { mk:incos11c,
    ABSTRACT = { Abstract---In this paper, we provide the idea of analyzing the performance of algorithms generating personal recommendation by using competing algorithms for one and the same recommendation request based on same situation and information in a unified framework. The analysis of the recently proposed collaborative filter PAF (Popularity Among Friends) for finding user similarity based on past ratings and evaluation of missing ratings of a user-item-matrix in order to generate recommendations will serve as a base to specify competing algorithm for an experimental recommendation system. We present results of an on-line experiment of the proposed recommendation system which will demonstrate the advantage of directly comparing the rate of user acceptance of competing algorithms and allow a statement about their suitability as base of an easy evaluation of the system. The evaluation gives a conclusion about algorithms to be replaced through new competing methods in order to steadily improve the recommendation system. },
    ADDRESS = { Fukuoka, Japan },
    AUTHOR = { Elnaz Mazandarani and Kaori Yoshida and Mario Köppen and Wladimir Bodrow },
    BOOKTITLE = { Proc. Third International Conference on Intelligent Networking and Collaborative Systems (INCoS 2011) },
    ADDED = { 2011-08-24 16:15:44 +0900 },
    MODIFIED = { 2011-12-14 14:38:28 +0900 },
    MONTH = { November },
    PAGES = { 857-862 },
    TITLE = { Recommendation System Based on Competing Algorithms },
    YEAR = { 2011 },
}