Aggregation functions for recommender systems

Beliakov, Gleb, Calvo, Tomasa and James, Simon 2015, Aggregation functions for recommender systems. In Ricci, F., Rokach, L. and Shapira, B. (ed), Recommender systems handbook, Springer, New York, N.Y., pp.777-808, doi: 10.1007/978-1-4899-7637-6_23.

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Title Aggregation functions for recommender systems
Author(s) Beliakov, GlebORCID iD for Beliakov, Gleb
Calvo, Tomasa
James, Simon
Title of book Recommender systems handbook
Editor(s) Ricci, F.
Rokach, L.
Shapira, B.
Publication date 2015
Chapter number 23
Total chapters 28
Start page 777
End page 808
Total pages 32
Publisher Springer
Place of Publication New York, N.Y.
Summary This chapter gives an overview of aggregation functions and their use in recommender systems. The classical weighted average lies at the heart of various recommendation mechanisms, often being employed to combine item feature scores or predict ratings from similar users. Some improvements to accuracy and robustness can be achieved by aggregating different measures of similarity or using an average of recommendations obtained through different techniques. Advances made in the theory of aggregation functions therefore have the potential to deliver increased performance to many recommender systems. We provide definitions of some important families and properties, sophisticated methods of construction, and various examples of aggregation functions in the domain of recommender systems.
ISBN 9781489976369
Language eng
DOI 10.1007/978-1-4899-7637-6_23
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
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