Hypothesis-based collaborative filtering: retrieving like-minded individuals based on the comparison of hypothesized preferences

Bouza, Amancio

doi:10.5167/uzh-73182

2012

DOI: 10.5167/uzh-73182

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Hypothesis-based collaborative filtering: retrieving like-minded individuals based on the comparison of hypothesized preferences

Amancio Bouza¹

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2014

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(Partial) user preference similarity as classification-based model similarity

Bouza

Bernstein

2014

Semantic Web

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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 classificationbased 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.

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(Partial) user preference similarity as classification-based model similarity

Bouza

Bernstein

2014

Semantic Web

View full text Add to dashboard Cite

show abstract

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Hypothesis-based collaborative filtering: retrieving like-minded individuals based on the comparison of hypothesized preferences

Cited by 1 publication

References 0 publications

(Partial) user preference similarity as classification-based model similarity

(Partial) user preference similarity as classification-based model similarity

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