Improving E-Commerce Recommender Systems by the Identification of Seasonal Products

Henrik Stormer

In recent years, the number of recommender systems used in online shops has strongly increased and is becoming an important success factor for electronic commerce applications. A recommender system can be utilized to suggest similar related or potentially interesting products for a given customer or a set of products for a marketing campaign. This paper shows how seasonal products can be identified and included in the recommendation process to improve the quality of a recommender system. The approach was tested using real life data from two companies selling working protection and tools.

Subjects: 1.10 Information Retrieval; 1.6 Engineering And Science

Submitted: May 9, 2007

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