Constraint-based recommender applications derive interesting products and services from large
and potentially complex item sets. If such applications cannot find a solution, repair actions are proposed
that provide support for getting out of the so-called “no solution could be found” dilemma. Such repairs
are not personalized and thus often cause a low level of satisfaction and cancellations of sessions. In this
work we introduce a personalized repair algorithm that integrates the concepts of model-based diagnosis
with collaborative recommendation approaches. For demonstration purposes, we present the results of a
study that show improvements in prediction accuracy when personalized repair actions are proposed.
%0 Conference Paper
%1 paper:felfernig:2009
%A Felfernig, Alexander
%A Schubert, Monika
%A Friedrich, Gerhard
%A Mairitsch, Markus
%A Mandl, Monika
%B DX-09 - 20th International Workshop on Principles of Diagnosis
%D 2009
%K diagnosis dx09 own repair
%P 315--320
%T Personalized Diagnosis and Repair of Customer Requirements in Constraint-based Recommendation
%X Constraint-based recommender applications derive interesting products and services from large
and potentially complex item sets. If such applications cannot find a solution, repair actions are proposed
that provide support for getting out of the so-called “no solution could be found” dilemma. Such repairs
are not personalized and thus often cause a low level of satisfaction and cancellations of sessions. In this
work we introduce a personalized repair algorithm that integrates the concepts of model-based diagnosis
with collaborative recommendation approaches. For demonstration purposes, we present the results of a
study that show improvements in prediction accuracy when personalized repair actions are proposed.
@inproceedings{paper:felfernig:2009,
abstract = {Constraint-based recommender applications derive interesting products and services from large
and potentially complex item sets. If such applications cannot find a solution, repair actions are proposed
that provide support for getting out of the so-called “no solution could be found” dilemma. Such repairs
are not personalized and thus often cause a low level of satisfaction and cancellations of sessions. In this
work we introduce a personalized repair algorithm that integrates the concepts of model-based diagnosis
with collaborative recommendation approaches. For demonstration purposes, we present the results of a
study that show improvements in prediction accuracy when personalized repair actions are proposed.},
added-at = {2009-09-28T14:15:36.000+0200},
author = {Felfernig, Alexander and Schubert, Monika and Friedrich, Gerhard and Mairitsch, Markus and Mandl, Monika},
biburl = {https://www.bibsonomy.org/bibtex/284fb74e5f89ae5344e6dab540d611e18/mschuber},
booktitle = {DX-09 - 20th International Workshop on Principles of Diagnosis},
interhash = {382a642356e80d2990cd392bdf2fb8e1},
intrahash = {84fb74e5f89ae5344e6dab540d611e18},
keywords = {diagnosis dx09 own repair},
pages = {315--320},
timestamp = {2009-09-28T14:15:37.000+0200},
title = {Personalized Diagnosis and Repair of Customer Requirements in Constraint-based Recommendation},
year = 2009
}