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Leveraging missing ratings to improve online recommendation systems

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"Product recommendation systems" have emerged as backbones of some of the Internet economy's largest and most venerable firms. A key feature of recommendation system data is the exceptionally large proportion of missing values; few customers rate more than a handful of items. For the EachMovie data, which has been widely used in prior studies, the authors find that missing data are strongly nonignorable. Recommendation quality can be improved substantially through a joint model for both "selection" and "ratings" that is, whether an item is rated and how it is rated. Furthermore, the authors find that such relationships can vary with customer demographics. Empirical results demonstrate that four modeling constructs - a nonignorable missing data mechanism, an individual-level account of the ordinal nature of ratings data, a reasonably sophisticated heterogeneity specification, and correlation between the underlying selection and ratings generation processes - can jointly substantially improve the accuracy of making product recommendations.Printed Journal

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Ketersediaan

Call Number	Location	Available
	PSB lt.dasar - Pascasarjana	1

Penerbit	American Marketing Association.,
Edisi	-
Subjek	Electronic commerce Models Product quality Correlation analysis studies Online advertising
ISBN/ISSN	222437
Klasifikasi	-
Deskripsi Fisik	-
Info Detail Spesifik	-
Other Version/Related	Tidak tersedia versi lain
Lampiran Berkas	Tidak Ada Data