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Comparing Recommendation Strategies in a Commercial Context
May/June 2007 (vol. 22 no. 3)
pp. 69-73
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.
Index Terms:
Recommender Systems, Personalization, Evaluation
Citation:
Markus Zanker, Markus Jessenitschnig, Dietmar Jannach, Sergiu Gordea, "Comparing Recommendation Strategies in a Commercial Context," IEEE Intelligent Systems, vol. 22, no. 3, pp. 69-73, May-June 2007, doi:10.1109/MIS.2007.49
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