Macro-optimization of email recommendation response rates harnessing individual activity levels and group affinity trends
| Autor: | Korayem, Mohammed, Aljadda, Khalifeh, Grainger, Trey |
|---|---|
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | The 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16) , 2016 |
| Druh dokumentu: | Working Paper |
| Popis: | Recommendation emails are among the best ways to re-engage with customers after they have left a website. While on-site recommendation systems focus on finding the most relevant items for a user at the moment (right item), email recommendations add two critical additional dimensions: who to send recommendations to (right person) and when to send them (right time). It is critical that a recommendation email system not send too many emails to too many users in too short of a time-window, as users may unsubscribe from future emails or become desensitized and ignore future emails if they receive too many. Also, email service providers may mark such emails as spam if too many of their users are contacted in a short time-window. Optimizing email recommendation systems such that they can yield a maximum response rate for a minimum number of email sends is thus critical for the long-term performance of such a system. In this paper, we present a novel recommendation email system that not only generates recommendations, but which also leverages a combination of individual user activity data, as well as the behavior of the group to which they belong, in order to determine each user's likelihood to respond to any given set of recommendations within a given time period. In doing this, we have effectively created a meta-recommendation system which recommends sets of recommendations in order to optimize the aggregate response rate of the entire system. The proposed technique has been applied successfully within CareerBuilder's job recommendation email system to generate a 50\% increase in total conversions while also decreasing sent emails by 72% Comment: This version is accepted as regular paper in The 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16) . pre-camera ready version |
| Databáze: | arXiv |
| Externí odkaz: |
|