NDLI: Using maximum coverage to optimize recommendation systems in e-commerce

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Using maximum coverage to optimize recommendation systems in e-commerce

Content Provider	ACM Digital Library
Author	Hammar, Mikael Nilsson, Bengt J. Karlsson, Robin
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.
Starting Page	265
Ending Page	272
Page Count	8
File Format	PDF
ISBN	9781450324090
DOI	10.1145/2507157.2507169
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Maximum coverage Recommendation systems E-commerce Optimization
Content Type	Text
Resource Type	Article

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