A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

TL;DR

This paper introduces an offline evaluation protocol for recommender systems in multi-carousel user interfaces, emphasizing the importance of context and complementarity among recommendation lists, with experiments showing how different models perform in this setting.

Contribution

The paper proposes a novel offline evaluation methodology for carousel-based recommender systems that considers the interaction among multiple recommendation lists.

Findings

Ranking of algorithms changes in carousel settings.

Matrix factorization models outperform item-based models with SLIM carousels.

Extended metrics account for position bias in multi-carousel layouts.

Abstract

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e. widgets or swipeable carousels, each built with a specific criterion (e.g. most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. This is not considered by traditional offline evaluation protocols. Hence, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We report experiments on publicly available datasets on the movie domain and notice that under a carousel setting the ranking of the algorithms change. In particular, when a SLIM carousel is available, matrix factorization models tend to be preferred, while item-based models are penalized. We also propose to extend ranking metrics to the two-dimensional carousel layout in order to account for a known position bias, i.e. users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen.