LEARNING TAKE A BREAK: SUSTAINABLE OPTIMIZATION OF LONG-TERM USER ENGAGEMENT

Abstract

Optimizing user engagement is a key goal for modern recommendation systems, but blindly pushing users towards increased consumption risks burn-out, churn, or even addictive habits. To promote digital well-being, most platforms now offer a service that periodically prompts users to take a break. These, however, must be set up manually, and so may be suboptimal for both users and the system. In this paper, we propose a framework for optimizing long-term engagement by learning individualized breaking policies. Using Lotka-Volterra dynamics, we model users as acting based on two balancing latent states: drive, and interest-which must be conserved. We then give an efficient learning algorithm, provide theoretical guarantees, and empirically evaluate its performance on semi-synthetic data.

1. INTRODUCTION

As consumers of content, we have come to rely extensively on algorithmic recommendations. This has made the task of recommending-in a relevant, timely, and personalized manner-key to the success of modern media platforms. Most commercial systems are built with the primary aim of optimizing user engagement, a process in which machine learning plays a central role. But alongside their many successes, recommendation systems have also been scrutinized for heedlessly driving users towards excessive and often undesired levels of consumption. This has raised awareness as to the need for redesigning recommendation systems in ways that actively promote digital well-being. How can media platforms balance business goals with user well-being? One prominent approach, which is now offered by most major platforms, is to periodically prompt users to take breaks (Constine, 2018; Perez, 2018) . The idea behind breaks is that occasional disruptions curb the inertial urge for perpetual consumption, and can therefore aid in reducing 'mindless scrolling' (Rauch, 2018) , or even addiction (Montag et al., 2018; Ding et al., 2016) . As a general means for promoting well-being, breaking is psychologically well-grounded (e.g., Danziger et al., 2011; Sievertsen et al., 2016) . But for platforms, breaks serve a utilitarian purpose: their goal is to foster long-term engagement by compensating for the myopic nature of conventional recommendation algorithms, which are typically trained to optimize immediate engagement. Since breaking schedules are applied heuristically on top of existing recommendation policies-and typically need to be set up manually by users-current solutions unlikely utilize their full potential (Monge Roffarello & De Russis, 2019). In this paper, we propose a disciplined learning framework for responsible and sustainable optimization of long-term user engagement by controlling breaks. Our point of departure is that sustained engagement necessitates sustained user well-being, and here we advocate for breaks as a means to establish both. Focusing on feed-based recommendation, our framework optimizes long-term engagement by learning an optimal breaking policy that prescribes individualized breaking schedules. The challenge in learning to break is that the effects of recommendations on users can slowly accumulate over time, deeming as ineffectual policies that rely on clear signs of over-exposure. To be preemptive, we argue that breaks must be scheduled in a way that anticipates the future trajectory of user behavior, and early on. To achieve this, we introduce a novel class of behavioral models based on Lokta-Volterra (LV) dynamical systems (Lotka, 1910) . These depict users as acting based on two balancing forces: drive to consume and intrinsic interest, with corresponding latent states. Intuitively, high interest increases drive to consume, but prolonged consumption decreases interest; together, these describe how user behavior varies over time and in response to recommendations. Our model captures the notion that interest can exhaust long before over-consumption is observed.

