An Experiment on Recommender Systems for SME Online Shops
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Authors
Stormer, Henrik
Werro, Nicolas
Risch, Daniel
Author (Corporation)
Publication date
2006
Typ of student thesis
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Collections
Type
04B - Conference paper
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Parent work
Proceedings of the 7th International We-B (Working for E-Business) Conference
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Volume
Issue / Number
Pages / Duration
150-157
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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. However the collaborative filtering technique does need a dense matrix in order to return pertinent recommendations. This paper proposes a way of combining several types of information in order to improve the density of the input matrix. The presented solution focuses on small and medium-sized online shops that can benefit from the presented results when they want to implement a recommender system in their application.
Keywords
B2C, Customer Profiles
Event
7th International We-B (Working for E-Business) Conference
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Language
English
Created during FHNW affiliation
Unknown
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Publication status
Published
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No peer review
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Citation
Stormer, H., Werro, N., & Risch, D. (2006). An Experiment on Recommender Systems for SME Online Shops. Proceedings of the 7th International We-B (Working for E-Business) Conference, 150–157. https://doi.org/10.26041/fhnw-2892