Comparing Simple Association-Rules and Repeat-Buying Based Recommender Systems in a B2B Environment

Autor:	Michael Hahsler, Anke Thede, Andreas Geyer-Schulz
Rok vydání:	2003
Předmět:	Association rule learning Computer science business.industry Recommender system Machine learning computer.software_genre Set (abstract data type) Empirical research Simple (abstract algebra) Outlier Artificial intelligence Sensitivity (control systems) Product (category theory) business computer
Zdroj:	Between Data Science and Applied Data Analysis ISBN: 9783540403548
DOI:	10.1007/978-3-642-18991-3_48
Popis:	In this contribution we present a systematic evaluation and comparison of recommender systems based on simple association rules and on repeat-buying theory. Both recommender services are based on the customer purchase histories of a medium-sized B2B-merchant for computer accessories. With the help of product managers an evaluation set for recommendations was generated. With regard to this evaluation set, recommendations produced by both methods are evaluated and several error measures are computed. This provides an empirical test whether frequent item sets or outliers of a stochastic purchase incidence model are suitable concepts for automatically generating recommendations. Furthermore, the loss functions (performance measures) of the two methods are compared and the sensitivity with regard to a misspecification of the model parameters is discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4127fea31a955668318a3ba10c08e14e https://doi.org/10.1007/978-3-642-18991-3_48 Zobrazit plný text záznamu