Employing document embeddings to solve the “new catalog” problem in user targeting, and provide explanations to the users

Abstract

In the current digital era, items that were consumed in a physical form are now available in online platforms that allow users to stream or buy them. However, not all of the items are available in digital form. When the companies that run these platforms acquire the rights to add a new catalog of items, the problem that arises is to identify who, among the customers, should be advertised with this new addition. Indeed, although the items may have existed for a long time, the preferences of the users for these items are not available. In this paper, we propose an approach that selects a set of users to target, to advertise a new catalog. In order to do so, we consider the textual description of these items and employ document embeddings (i.e., vector representations of a document) to model both the new catalog and the users. We also propose an approach to generate an explanation list to a user, represented by the top-n artists she evaluated that are most similar to the one of the new catalog. Experimental results show the effectiveness of both our targeting approach and of the explanation lists.