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Using Maximum Cover...
Using Maximum Coverage to Optimize Recommendation Systems in E-Commerce
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- Hammar, Mikael (författare)
- Apptus Technologies, Lund, Sweden
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- Karlsson, Robin (författare)
- Apptus Technologies, Lund, Sweden
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- Nilsson, Bengt J. (författare)
- Malmö högskola,Teknik och samhälle (TS)
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(creator_code:org_t)
- 2013-10-12
- 2013
- Engelska.
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Ingår i: Proceedings of the 7th ACM conference on Recommender systems. - New York, NY, USA : ACM Digital Library. - 9781450324090 ; , s. 265-272
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Abstract
Ämnesord
Stäng
- 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)
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