Case-studies on exploiting explicit customer requirements in recommender systems Export

@article{citeulike:3634542, abstract = {Abstract\ \ Recommender Systems (RS) suggest useful and interesting items to users in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. This paper focuses on explicitly formulated customer requirements as the sole type of user feedback. Its contribution lies in comparing different techniques such as knowledge- and utility-based methods, collaborative filtering, association rule mining as well as hybrid variants when user models consist solely of explicit customer requirements. We examine how this type of user feedback can be exploited for personalization in e-commerce scenarios. Furthermore, examples of actual online shops are developed where such contextual user information is available, demonstrating how more efficient RS configurations can be implemented. Results indicate that, especially for new users, explicit customer requirements are a useful source of feedback for personalization and hybrid configurations of collaborative and knowledge-based techniques achieve best results.}, author = {Zanker, Markus and Jessenitschnig, Markus}, citeulike-article-id = {3634542}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1499116.1499124}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s11257-008-9048-y}, citeulike-linkout-2 = {http://www.springerlink.com/content/f6q76751605r1221}, day = {1}, doi = {10.1007/s11257-008-9048-y}, issn = {0924-1868}, journal = {User Modeling and User-Adapted Interaction}, keywords = {e-commerce, recommendation}, month = {February}, number = {1}, pages = {133--166}, posted-at = {2009-08-17 14:22:37}, priority = {2}, title = {Case-studies on exploiting explicit customer requirements in recommender systems}, url = {http://dx.doi.org/10.1007/s11257-008-9048-y}, volume = {19}, year = {2009} }

TY - JOUR ID - citeulike:3634542 L3 - citeulike-article-id:3634542 N2 - Abstract  Recommender Systems (RS) suggest useful and interesting items to users in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. This paper focuses on explicitly formulated customer requirements as the sole type of user feedback. Its contribution lies in comparing different techniques such as knowledge- and utility-based methods, collaborative filtering, association rule mining as well as hybrid variants when user models consist solely of explicit customer requirements. We examine how this type of user feedback can be exploited for personalization in e-commerce scenarios. Furthermore, examples of actual online shops are developed where such contextual user information is available, demonstrating how more efficient RS configurations can be implemented. Results indicate that, especially for new users, explicit customer requirements are a useful source of feedback for personalization and hybrid configurations of collaborative and knowledge-based techniques achieve best results. IS - 1 JF - User Modeling and User-Adapted Interaction SN - 0924-1868 EP - 166 TI - Case-studies on exploiting explicit customer requirements in recommender systems VL - 19 SP - 133 KW - e-commerce KW - recommendation AU - Zanker, Markus AU - Jessenitschnig, Markus PY - 2009/02/01/ UR - http://dx.doi.org/10.1007/s11257-008-9048-y ER -