Online Evaluation Methods for the Causal Effect of Recommendations
This addresses the challenge of costly and risky A/B testing for recommendation systems, benefiting developers and businesses by enabling more efficient model selection.
The paper tackles the problem of efficiently evaluating the causal effect of recommendations, which is costly with traditional A/B testing, by proposing the first interleaving methods that measure outcomes for both recommended and non-recommended items, resulting in unbiased and more efficient evaluations compared to A/B testing.
Evaluating the causal effect of recommendations is an important objective because the causal effect on user interactions can directly leads to an increase in sales and user engagement. To select an optimal recommendation model, it is common to conduct A/B testing to compare model performance. However, A/B testing of causal effects requires a large number of users, making such experiments costly and risky. We therefore propose the first interleaving methods that can efficiently compare recommendation models in terms of causal effects. In contrast to conventional interleaving methods, we measure the outcomes of both items on an interleaved list and items not on the interleaved list, since the causal effect is the difference between outcomes with and without recommendations. To ensure that the evaluations are unbiased, we either select items with equal probability or weight the outcomes using inverse propensity scores. We then verify the unbiasedness and efficiency of online evaluation methods through simulated online experiments. The results indicate that our proposed methods are unbiased and that they have superior efficiency to A/B testing.