Product Collection Recommendation in Online Retail

In this paper we combine transactional data with domain expertise to improve bundle recommendation of products that share a common theme in the retail industry.

Authors: Pigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Nian Yan, Unaiza Ahsan, Khalifeh Al Jadda, Huiming Qu. 2019.

In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys ‘19).

Recommender systems are an integral part of eCommerce services, helping to optimize revenue and user satisfaction. Bundle recommendation has recently gained attention by the research community since behavioral data supports that users often buy more than one product in a single transaction. In most cases, bundle recommendations are of the form “users who bought product A also bought products B, C, and D”. Although such recommendations can be useful, there is no guarantee that products A,B,C, and D may actually be related to each other. In this paper, we address the problem of collection recommendation, i.e., recommending a collection of products that share a common theme and can potentially be purchased together in a single transaction.