Addressing the cold user problem for model-based recommender systems

Tomas Geurts, Flavius Frasincar

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)

Abstract

Customers of a web shop are often presented large assortments, which can lead to customers struggling finding their desired product( s), an issue known as choice overload. In order to overcome this issue, recommender systems are used in webshops to provide personalized product recommendations to customers. Though, recommender systems using matrix factorization are not able to provide recommendations to new customers (i.e., cold users). To facilitate recommendations to cold users we investigate multiple active learning strategies, and subsequently evaluate which active learning strategy is able to optimally elicit the preferences from the cold users. Our model is empirically validated using a dataset from the webshop of de Bijenkorf, a Dutch department store. We find that the overall best-performing active learning strategy is PopGini, an active learning strategy which combines the popularity of an item with its Gini impurity score.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
Pages745-752
Number of pages8
ISBN (Electronic)9781450349512
DOIs
Publication statusPublished - 23 Aug 2017
Event16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 - Leipzig, Germany
Duration: 23 Aug 201726 Aug 2017

Publication series

SeriesProceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017

Conference

Conference16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017
Country/TerritoryGermany
CityLeipzig
Period23/08/1726/08/17

Bibliographical note

Publisher Copyright:
© 2017 ACM.

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