Comparative Evaluation of Top-N Recommenders in e-Commerce : an Industrial Perspective

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Comparative Evaluation of Top-N Recommenders in e-Commerce : an Industrial Perspective

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Publication Conference Paper, peer reviewed
Title Comparative Evaluation of Top-N Recommenders in e-Commerce : an Industrial Perspective
Author(s) Paraschakis, Dimitris ; Nilsson, Bengt ; Holländer, John
Date 2015
English abstract
We experiment on two real e-commerce datasets and survey more than 30 popular e-commerce platforms to reveal what methods work best for product recommendations in industrial settings. Despite recent academic advances in the field, we observe that simple methods such as best-seller lists dominate deployed recommendation engines in e-commerce. We find our empirical findings to be well-aligned with those of the survey, where in both cases simple personalized recommenders achieve higher ranking than more advanced techniques. We also compare the traditional random evaluation protocol to our proposed chronological sampling method, which can be used for determining the optimal time-span of the training history for optimizing the performance of algorithms. This performance is also affected by a proper hyperparameter tuning, for which we propose golden section search as a fast alternative to other optimization techniques.
Publisher IEEE
Host/Issue IEEE Xplore;
Language eng (iso)
Subject(s) recommender systems
recommenations
collaborative filtering
e-commerce
recommender systems survey
matrix factorization
golden section search
evaluation of recommender systems
Technology
Research Subject Categories::TECHNOLOGY
Note 14th IEEE International Conference on Machine Learning and Applications December 9-11, 2015 Miami, Florida, USA
Handle http://hdl.handle.net/2043/19943 (link to this page)
Link http://www.icmla-conference.org/icmla15/ (external link to related web page)

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