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We provide a method to determine whether a new recommendation system improves the revenue per visit (RPV) compared to the status quo. We achieve our goal by splitting RPV into conversion rate and average order value (AOV). We use the two-part test suggested by Lachenbruch to determine if the data generating process in the new system is different. In cases that this test does not give us a definitive answer about the change in RPV, we propose two alternative tests to determine if RPV has changed. Both of these tests rely on the assumption that non-zero purchase values follow a log-normal distribution. We empirically validate this assumption using data collected at different points in time from Staples.com. On average, our method needs a smaller sample size than other methods. Furthermore, it does not require any subjective outlier removal. Finally, it characterizes the uncertainty around RPV by providing a confidence interval.
Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in interactive recommender systems (IRSs). IRSs usually face the large discrete action spa
The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely used in real application scenarios. Previous interactive recommendatio
Different from shopping at retail stores, consumers on e-commerce platforms usually cannot touch or try products before purchasing, which means that they have to make decisions when they are uncertain about the outcome (e.g., satisfaction level) of p
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., ses
Citation recommendation systems for the scientific literature, to help authors find papers that should be cited, have the potential to speed up discoveries and uncover new routes for scientific exploration. We treat this task as a ranking problem, wh