An increasing number of media streaming services have expanded their offerings to include entities of multiple content types. For instance, audio streaming services that started by offering music only, now also offer podcasts, merchandise items, and videos. Ranking items across different content types into a single slate poses a significant challenge for traditional learning-to-rank (LTR) algorithms due to differing user engagement patterns for different content types. We explore a simple method for cross-content-type ranking, called multinomial blending (MB), which can be used in conjunction with most existing LTR algorithms. We compare MB to existing baselines not only in terms of ranking quality but also from other industry-relevant perspectives such as interpretability, ease-of-use, and stability in dynamic environments with changing user behavior and ranking model retraining. Finally, we report the results of an A/B test from an Amazon Music ranking use-case.

Evaluating a new ranking policy using data logged by a previously deployed policy requires a counterfactual (off-policy) estimator that corrects for presentation and selection biases. Some estimators (e.g., the position-based model) perform this correction by making strong assumptions about user behavior, which can lead to high bias if the assumptions are not met. Other estimators (e.g., the item-position model) rely on randomization to avoid these assumptions, but they often suffer from high variance. In this paper, we develop a new counterfactual estimator, called Interpol, that provides a tunable trade-off in the assumptions it makes, thus providing a novel ability to optimize the bias-variance trade-off. We analyze the bias of our estimator, both theoretically and empirically, and show that it achieves lower error than both the position-based model and the item-position model, on both synthetic and real datasets. This improvement in accuracy not only benefits offline evaluation of ranking policies, we also find that Interpol improves learning of new ranking policies when used as the training objective for learning-to-rank.

Feature allocation models are popular models used in different applications such as unsupervised learning or network modeling. In particular, the Indian buffet process is a flexible and simple one-parameter feature allocation model where the number of features grows unboundedly with the number of objects. The Indian buffet process, like most feature allocation models, satisfies a symmetry property of exchangeability: the distribution is invariant under permutation of the objects. While this property is desirable in some cases, it has some strong implications. Importantly, the number of objects sharing a particular feature grows linearly with the number of objects. In this article, we describe a class of non-exchangeable feature allocation models where the number of objects sharing a given feature grows sublinearly, where the rate can be controlled by a tuning parameter. We derive the asymptotic properties of the model, and show that such models provides a better fit and better predictive performances on various datasets.