Issue No. 03 - May/June (2007 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIS.2007.49
Markus Zanker , University Klagenfurt
Markus Jessenitschnig , eTourism Competence Center Austria
Dietmar Jannach , University Klagenfurt
Sergiu Gordea , University Klagenfurt
Recommender systems have a long tradition of reducing users' search costs by proposing items on the basis of users' preferences and aggregated information about other users. In e-commerce scenarios, different types of user preferences—implicitly collected ratings as well as explicitly formulated requirements—are available. The authors perform a comparative evaluation across different recommendation techniques, such as knowledge-based sales advisory and collaborative filtering, on a commercial data set. By making this data set publicly available, the authors hope to foster research efforts on the specific requirements of commercial shopping platforms.
Recommender Systems, Personalization, Evaluation
M. Zanker, D. Jannach, M. Jessenitschnig and S. Gordea, "Comparing Recommendation Strategies in a Commercial Context," in IEEE Intelligent Systems, vol. 22, no. , pp. 69-73, 2007.