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Enhancing the scalability of distance-based link prediction algorithms in recommender systems throug...

Enhancing the scalability of distance-based link prediction algorithms in recommender systems throug...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2695866199

Enhancing the scalability of distance-based link prediction algorithms in recommender systems through similarity selection

About this item

Full title

Enhancing the scalability of distance-based link prediction algorithms in recommender systems through similarity selection

Publisher

San Francisco: Public Library of Science

Journal title

PloS one, 2022-07, Vol.17 (7), p.e0271891-e0271891

Language

English

Formats

Publication information

Publisher

San Francisco: Public Library of Science

More information

Scope and Contents

Contents

Slope One algorithm and its descendants measure user-score distance and use the statistical score distance between users to predict unknown ratings, as opposed to the typical collaborative filtering algorithm that uses similarity for neighbor selection and prediction. Compared to collaborative filtering systems that select only similar neighbors, a...

Alternative Titles

Full title

Enhancing the scalability of distance-based link prediction algorithms in recommender systems through similarity selection

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_plos_journals_2695866199

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2695866199

Other Identifiers

ISSN

1932-6203

E-ISSN

1932-6203

DOI

10.1371/journal.pone.0271891

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