Header

UZH-Logo

Maintenance Infos

Collaborative filtering or regression models for internet recommendation systems?


Mild, Andreas; Natter, Martin (2002). Collaborative filtering or regression models for internet recommendation systems? Journal of Targeting, Measurement and Analysis for Marketing, 10(4):304-313.

Abstract

The literature on recommendation systems indicates that the choice of the methodology significantly influences the quality of recommendations. The impact of the amount of available data on the performance of recommendation systems has not been systematically investigated. The authors study different approaches to recommendation systems using the publicly available EachMovie data set containing ratings for movies and videos. In contrast to previous work on this data set, here a significantly larger subset is used. The effects caused by the available number of customers and movies as well as their interaction with different methods are investigated. Two commonly used collaborative filtering approaches are compared with several regression models using an experimental full factorial design. According to the findings, the number of customers significantly influences the performance of all approaches under study. For a large number of customers and movies, it is shown that simple linear regression with model selection can provide significantly better recommendations than collaborative filtering. From a managerial perspective, this gives suggestions about the selection of the model to be used depending on the amount of data available. Furthermore, the impact of an enlargement of the customer database on the quality of recommendations is shown.

Abstract

The literature on recommendation systems indicates that the choice of the methodology significantly influences the quality of recommendations. The impact of the amount of available data on the performance of recommendation systems has not been systematically investigated. The authors study different approaches to recommendation systems using the publicly available EachMovie data set containing ratings for movies and videos. In contrast to previous work on this data set, here a significantly larger subset is used. The effects caused by the available number of customers and movies as well as their interaction with different methods are investigated. Two commonly used collaborative filtering approaches are compared with several regression models using an experimental full factorial design. According to the findings, the number of customers significantly influences the performance of all approaches under study. For a large number of customers and movies, it is shown that simple linear regression with model selection can provide significantly better recommendations than collaborative filtering. From a managerial perspective, this gives suggestions about the selection of the model to be used depending on the amount of data available. Furthermore, the impact of an enlargement of the customer database on the quality of recommendations is shown.

Statistics

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
Dewey Decimal Classification:330 Economics
Language:English
Date:2002
Deposited On:09 Dec 2016 11:00
Last Modified:09 Dec 2016 11:00
Publisher:Palgrave Macmillan Ltd.
ISSN:0967-3237
Publisher DOI:https://doi.org/10.1057/palgrave.jt.5740055
Other Identification Number:merlin-id:14207

Download

Full text not available from this repository.
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations