Top-N recommendations from expressive recommender systems

TL;DR

This paper evaluates normalized nonnegative models for top-N recommendations, demonstrating they are as effective as existing methods like PureSVD while offering interpretable representations and theoretical insights.

Contribution

It introduces normalized nonnegative models for recommender systems, analyzing their performance, interpretability, and theoretical properties, including NP-hardness and relationships to matrix factorization.

Findings

Normalized nonnegative models match PureSVD in recommendation accuracy.

They provide interpretable user and item representations.

Inference of optimal models is NP-hard.

Abstract

Normalized nonnegative models assign probability distributions to users and random variables to items; see [Stark, 2015]. Rating an item is regarded as sampling the random variable assigned to the item with respect to the distribution assigned to the user who rates the item. Models of that kind are highly expressive. For instance, using normalized nonnegative models we can understand users' preferences as mixtures of interpretable user stereotypes, and we can arrange properties of users and items in a hierarchical manner. These features would not be useful if the predictive power of normalized nonnegative models was poor. Thus, we analyze here the performance of normalized nonnegative models for top-N recommendation and observe that their performance matches the performance of methods like PureSVD which was introduced in [Cremonesi et al., 2010]. We conclude that normalized nonnegative models not only provide accurate recommendations but they also deliver (for free) representations that are interpretable. We deepen the discussion of normalized nonnegative models by providing further theoretical insights. In particular, we introduce total variational distance as an operational similarity measure, we discover scenarios where normalized nonnegative models yield unique representations of users and items, we prove that the inference of optimal normalized nonnegative models is NP-hard and finally, we discuss the relationship between normalized nonnegative models and nonnegative matrix factorization.