Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this user or item. Various solutions to this `cold-start problem' have been proposed in the literature. However, many real-life e-commerce applications suffer from an aggravated, recurring version of cold-start even for known users or items, since many users visit the website rarely, change their interests over time, or exhibit different personas. This paper exposes the `Continuous Cold Start' (CoCoS) problem and its consequences for content- and context-based recommendation from the viewpoint of typical e-commerce applications, illustrated with examples from a major travel recommendation website, Booking.com. Keywords: Recommender systems, continous cold-start problem, industrial applications
|Title of host publication||2nd Workshop on New Trends on Content-Based Recommender Systems (CBRecSys 2015, Vienna, Austria, September 20, 2015; co-located with RecSys 2015)|
|Editors||T. Bogers, M. Koolen|
|Publication status||Published - 2015|
|Name||CEUR Workshop Proceedings|
Bernardi, L., Kamps, J., Kiseleva, Y., & Mueller, M. J. I. (2015). The continuous cold start problem in e-commerce recommender systems. In T. Bogers, & M. Koolen (Eds.), 2nd Workshop on New Trends on Content-Based Recommender Systems (CBRecSys 2015, Vienna, Austria, September 20, 2015; co-located with RecSys 2015) (pp. 30-33). (CEUR Workshop Proceedings; Vol. 1448). CEUR-WS.org.