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. We study different approaches to recommendation systems using the publicly available EachMovie data set. In contrast to previous work on this data set, here a significantly higher subset is used. The effects caused by the number of customers and movies as well as their interaction with different methods are investigated. We compare two commonly used collaborative filtering approaches to several regression models using an experimental full factorial design. According to our findings, the number of customers significantly influences the performance of all approaches under study. For a large number of customers and movies, we show 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. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Zugl.: München, Univ., Diplomarbeit, 2005

Zugl.: Mannheim, Univ. Diss., 2007

Zugl.: München, Univ., Diplomarbeit, 2005

Zugl.: Saarbrücken, Univ., Diss., 2008.

Diss--Universität des Saarlandes.

Zugl.: Passau, Universiẗat, Diss.

Fachhochschule, Diplomarbeit--Stuttgart, 2005.

Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/nonchoice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"