%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 }