Multi-objective Evolutionary Rank Aggregation for Recommender Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Oliveira:2018:CEC,
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author = "Samuel Oliveira and Victor Diniz and
Anisio Lacerda and Gisele L. Pappa",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Multi-objective Evolutionary Rank Aggregation for
Recommender Systems",
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year = "2018",
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month = jul,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2018.8477669",
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abstract = "Recommender systems help users to overcome the
information overload problem by selecting relevant
items according to their preferences. This paper deals
with the problem of rank aggregation in recommender
systems, where we want to generate a single consensus
ranking from a given set of input rankings generated by
different recommendation algorithms. This problem is
NP-hard, and hence the use of meta-heuristics to solve
it is appealing. Although accurate suggestions are
mandatory for effective recommender systems, other
recommendation quality measures need to be taken into
account for delivering high-quality suggestions. This
paper proposes Multi-objective Evolutionary Rank
Aggregation (MERA), a genetic programming algorithm
following the concepts of SPEA2 that considers three
measures when suggesting items to users, namely mean
average precision, diversity, and novelty. The method
was tested in 3 real world recommendation datasets, and
the results show MERA can indeed find a balance for
these metrics while generating a diverse set of
solutions to the problem. MERA was able to return
solutions with improvements of up to 15percent in
diversity (for the Movie lens 1M dataset) and 7percent
in novelty (for the Film trust dataset) while
maintaining, or even improving, the values of
precision.",
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notes = "Also known as \cite{8477669}",
- }
Genetic Programming entries for
Samuel Oliveira
Victor Diniz
Anisio Lacerda
Gisele L Pappa
Citations