Abstract
One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.
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Notes
Pu et al. (2011) claim that these criteria are very hard or even impossible to measure. Moreover, it was shown by Jannach et al. (2013) that the choice of measurable accuracy criteria to evaluate RS is not easy and that objective evaluation measures and users’ subjective quality measures often disagree.
It is assumed only one feedback value for a user-item pair, here.
An offspring is created by adding small (normally distributed) values to each parameter of the parent individual.
See https://www.fit.fraunhofer.de/en/fb/cscw/projects/mace.html for more details, however, the data seems to be not publicly available.
In IEA, often applied to problems that are difficult to evaluate quantitatively, the fitness values of candidate solutions are based on the evaluation of a user according to her own interests.
Experimental settings are not clearly provided in the paper.
The authors only considered recommendation baselines, here. Even if some papers presenting EC-based clustering compare their results to other clustering techniques (e.g. k-means), they lack comparison to some simple recommendation baselines.
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This review was supported by the Brazilian research funding agencies CAPES, CNPq and FAPESP and the project VEGA 1/0475/14 granted by the Scientific Grant Agency of the Ministry of Education of Slovak Republic and the Slovak Academy of Sciences.
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This work was done under the project entitled “Utilizing Nature Inspired Computation Techniques in Recommender Systems” financed by the program PNPD/CAPES.
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Horváth, T., de Carvalho, A.C.P.L.F. Evolutionary computing in recommender systems: a review of recent research. Nat Comput 16, 441–462 (2017). https://doi.org/10.1007/s11047-016-9540-y
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DOI: https://doi.org/10.1007/s11047-016-9540-y