1. (Partial) user preference similarity as classification-based model similarity.
- Author
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Bouza, Amancio and Bernstein, Abraham
- Subjects
RECOMMENDER systems ,SIMILARITY (Psychology) ,CLASSIFICATION ,MACHINE learning ,DATA - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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