Long Term Recommender Benchmarking for Mobile Shopping List Applications using Markov Chains
Type
conference paper
Date Issued
2014-10-06
Author(s)
Abstract
This paper presents a method to estimate the performance and success rate of a recommender system for digital shopping lists. The list contains a number of items that are allowed to occupy three different states (to be purchased, purchased and deleted) as a function of time. Using Markov chains, the probability distribution function over time can be estimated for each state, and thus, the probability that a recommendation is deleted from the list can be used to benchmark a recommender on its endurance and performance. An experimental set up is described that allows to test the presented method in an actual mobile application. The application of the method will allow to benchmark a variety of recommenders. An outlook is given on how the presented method can be used iteratively to support a recommender in finding the user's favorite items/products.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Association for Computing Machinery
Start page
2
Event Title
8th ACM Recommender Systems Conference (RecSys)
Event Location
Silicon Valley, USA
Event Date
06.-10.10.2014
Subject(s)
Division(s)
Eprints ID
239223
File(s)![Thumbnail Image]()
Name
paper.pdf
Size
192.88 KB
Format
Adobe PDF
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