Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.
Description
A collaborative constraint-based meta-level recommender
%0 Conference Paper
%1 paper:zanker:2008
%A Zanker, Markus
%B RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
%C New York, NY, USA
%D 2008
%I ACM
%K 2008 RecSys RecSys08 collaborative-filtering
%P 139--146
%R http://doi.acm.org/10.1145/1454008.1454032
%T A collaborative constraint-based meta-level recommender
%U http://portal.acm.org/citation.cfm?id=1454032
%X Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.
%@ 978-1-60558-093-7
@inproceedings{paper:zanker:2008,
abstract = {Recommender Systems (RS) have become popular for their ability to make useful suggestions to online shoppers. Knowledge-based RS represent one branch of these types of applications that employ means-end knowledge to map abstract user requirements to product characteristics. Before setting up such a system, the knowledge has to be acquired from domain experts and formalized using constraints or a comparable representation mechanism. However, the initial acquisition of the knowledge base and its maintenance are effort intensive tasks. Here, we propose a system that learns rule-based preferences from successful interactions in historic transaction data. It is realized as a meta-level hybrid that employs collaborative filtering to derive preferences from a user's nearest neighbors that are processed by a knowledge-based RS to derive recommendations. An evaluation using a commercial dataset showed that this approach outperforms the prediction accuracy of a knowledge base provided by domain experts. In addition, the approach is applicable for supporting domain experts in the maintenance and validation tasks associated with providing personalization knowledge bases.},
added-at = {2009-03-03T09:33:00.000+0100},
address = {New York, NY, USA},
author = {Zanker, Markus},
biburl = {https://www.bibsonomy.org/bibtex/26f73f97245c5642fa827b17b8495fa74/mschuber},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
description = {A collaborative constraint-based meta-level recommender},
doi = {http://doi.acm.org/10.1145/1454008.1454032},
interhash = {9e0fd25046971edb737c871656433143},
intrahash = {6f73f97245c5642fa827b17b8495fa74},
isbn = {978-1-60558-093-7},
keywords = {2008 RecSys RecSys08 collaborative-filtering},
location = {Lausanne, Switzerland},
pages = {139--146},
publisher = {ACM},
timestamp = {2009-05-08T14:07:15.000+0200},
title = {A collaborative constraint-based meta-level recommender},
url = {http://portal.acm.org/citation.cfm?id=1454032},
year = 2008
}