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.
|Title of host publication||Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017|
|Number of pages||8|
|Publication status||Published - 23 Aug 2017|
|Event||16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017 - Leipzig, Germany|
Duration: 23 Aug 2017 → 26 Aug 2017
|Series||Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017|
|Conference||16th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017|
|Period||23/08/17 → 26/08/17|
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