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2016 | OriginalPaper | Buchkapitel

Recommending with Higher-Order Factorization Machines

verfasst von : Julian Knoll

Erschienen in: Research and Development in Intelligent Systems XXXIII

Verlag: Springer International Publishing

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Abstract

The accumulated information about customers collected by the big players of internet business is incredibly large. The main purpose of collecting these data is to provide customers with proper offers in order to gain sales and profit. Recommender systems cope with those large amounts of data and have thus become an important factor of success for many companies. One promising approach to generate capable recommendations are Factorization Machines. This paper presents an approach to extend the basic 2-way Factorization Machine model with respect to higher-order interactions. We show how to implement the necessary additional term for 3-way interactions in the model equation in order to retain the advantage of linear complexity. Furthermore, we carry out a simulation study which demonstrates that modeling 3-way interactions improves the prediction quality of a Factorization Machine.

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Titel: Recommending with Higher-Order Factorization Machines
verfasst von: Julian Knoll
Verlag: Springer International Publishing
Buch: Research and Development in Intelligent Systems XXXIII
Print ISBN: 978-3-319-47174-7

Electronic ISBN: 978-3-319-47175-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-47175-4_7

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