Using Maximum Coverage to Optimize Recommendation Systems in E-Commerce

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Using Maximum Coverage to Optimize Recommendation Systems in E-Commerce

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Publication Conference Paper, peer reviewed
Title Using Maximum Coverage to Optimize Recommendation Systems in E-Commerce
Author(s) Hammar, Mikael ; Karlsson, Robin ; Nilsson, Bengt J.
Date 2013
English abstract
We study the problem of optimizing recommendation systems for e-commerce sites. We consider in particular a combinatorial solution to this optimization based on the well known Maximum Coverage problem that asks for the k sets (products) that cover the most elements from a ground set (consumers). This formulation provides an abstract model for what k products should be recommended to maximize the probability of consumer purchase. Unfortunately, Maximum Coverage is NP-complete but an efficient approximation algorithm exists based on the Greedy methodology. We exhibit test results from the Greedy method on real data sets showing 3-8% increase in sales using the Maximum Coverage optimization method in comparison to the standard best-seller list. A secondary effect that our Greedy algorithm exhibits on the tested data is increased diversification in presented products over the best-seller list.
Publisher ACM
Host/Issue Proceedings of the 7th ACM conference on Recommender systems;
ISBN 978-1-4503-2409-0
Pages 265-272
Language eng (iso)
Subject(s) e-commerce
Information filtering
Maximum coverage
Optimization
Recommendation systems
Retrieval models
Search process
Technology
Research Subject Categories::TECHNOLOGY
Note The ACM conference series on Recommender systems (RecSys) Hong Kong, 12-16 October, 2013
Handle http://hdl.handle.net/2043/16856 (link to this page)
Link http://recsys.acm.org/recsys13/ (external link to related web page)

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