PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Applying collaborative filtering to real0life corporate data
Miha Grcar, Dunja Mladenić and Marko Grobelnik
In: 29th Annual Conference of the German Classification Society, 9-11 March 2005, Magdeburg, Germany.

Abstract

In this paper, we present our experience in applying collaborative filtering to real-life corporate data. The quality of collaborative filtering recommendations is highly dependent on the quality of the data used to identify users’ preferences. To understand the influence that highly sparse server-side collected data has on the accuracy of collaborative filtering, we ran a series of experiments in which we used publicly available datasets and, on the other hand, a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:1422
Deposited By:Dunja Mladenić
Deposited On:28 November 2005