Not logged in.

Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title (Partial) user preference similarity as classification-based model similarity
Organization Unit
  • Amancio Bouza
  • Abraham Bernstein
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Semantic Web
Publisher IOS Press
Geographical Reach international
ISSN 1570-0844
Volume 5
Number 1
Page Range 47 - 64
Date 2014
Abstract Text Recommender systems play an important role in helping people finding items they like. One type of recommender system is collaborative filtering that considers feedback of like-minded people. The fundamental assumption of collaborative filtering is that people who previously shared similar preferences behave similarly later on. This paper introduces several novel, classification-based similarity metrics that are used to compare user preferences. Furthermore, the concept of partial preference similarity based on a machine learning model is presented. For evaluation the cold-start behavior of the presented classification-based similarity metrics is evaluated in a large-scale experiment. It is shown that classification-based similarity metrics with machine learning significantly outperforms other similarity approaches in different cold-start situations under different degrees of data-sparseness.
Free access at Related URL
Official URL
Digital Object Identifier 10.3233/SW-130099
Other Identification Number merlin-id:7938
PDF File Download from ZORA
Export BibTeX