Don’t recommend the obvious: estimate probability ratios

Sequential recommender systems are becoming widespread in the online retail and streaming industry. These systems are often trained to predict the next item given a sequence of a user’s recent actions, and standard evaluation metrics reward systems that can identify the most probable items that might appear next. However, some recent papers instead evaluate recommendation systems with popularity-sampled metrics, which measure how well the model can find a user’s next item when hidden amongst generally-popular items. We argue that these popularity-sampled metrics are more appropriate for recommender systems, because the most probable items for a user often include generally-popular items. If the probability that a customer will watch Toy Story is not much more probable than for the average customer, then the movie isn’t especially relevant for them and we should not recommend it. This paper shows that optimizing popularity-sampled metrics is closely related to estimating point-wise mutual information (PMI). We propose and compare two techniques to fit PMI directly, which both improve popularity-sampled metrics for state-of-the-art recommender systems. The improvements are large compared to differences between recently-proposed model architectures.