Modeling User Retention through Generative Flow Networks

TL;DR

This paper introduces a flow-based probabilistic model to optimize user retention in recommender systems, effectively linking session satisfaction with individual item recommendations to improve long-term engagement.

Contribution

It proposes a novel flow-based approach to estimate user retention signals and back-propagate rewards to optimize recommendation strategies for better user retention.

Findings

Improved offline ranking metrics on public datasets.

Enhanced user retention in online A/B testing.

Effective back-propagation of retention signals to individual items.

Abstract

Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed out that how frequently the users come back for the service also reflects the quality and stability of recommendations. However, optimizing this user retention behavior is non-trivial and poses several challenges including the intractable leave-and-return user activities, the sparse and delayed signal, and the uncertain relations between users' retention and their immediate feedback towards each item in the recommendation list. In this work, we regard the retention signal as an overall estimation of the user's end-of-session satisfaction and propose to estimate this signal through a probabilistic flow. This flow-based modeling technique can back-propagate the retention reward towards each recommended item in the user session, and we show that the flow combined with traditional learning-to-rank objectives eventually optimizes a non-discounted cumulative reward for both immediate user feedback and user retention. We verify the effectiveness of our method through both offline empirical studies on two public datasets and online A/B tests in an industrial platform.