Collaborative Recommender Systems for Online Shops
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Authors
Stormer, Henrik
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Publication date
2007
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04B - Conference paper
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Proceedings of the 13th Americas Conference on Information Systems
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Abstract
Recommender systems are often used in electronic shops in order to suggest similar or related products, potentially interesting products for a given customer or a set of products for a marketing campaign. Most recommender systems use the collaborative filtering method in order to provide the personalization information. The collaborative filtering method is a very efficient and convenient way of achieving personalization as there is no need to introduce semantic information about the products or to manually link products and users together. In the last years, a number of optimizations for collaborative filtering techniques have been developed. This paper collects the ideas and shows which of them could be integrated successfully in order to optimize a collaborative recommender system for online shops.
Keywords
B2C, CRM, Customer Profiles
Subject (DDC)
330 - Wirtschaft
005 - Computer Programmierung, Programme und Daten
005 - Computer Programmierung, Programme und Daten
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13th Americas Conference on Information Systems
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German
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Unknown
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Published
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No peer review
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Citation
LEIMSTOLL, Uwe und Henrik STORMER, 2007. Collaborative Recommender Systems for Online Shops. In: Proceedings of the 13th Americas Conference on Information Systems. 2007. Verfügbar unter: https://doi.org/10.26041/fhnw-3033