Using Maximum Coverage to Optimize Recommendation Systems in E-Commerce

• 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

e-commerce

Information filtering

Maximum coverage

Optimization

Recommendation systems

Retrieval models

Search process

Publikations- och innehållstyp

vet (ämneskategori)

kon (ämneskategori)