Surrogate for Long-Term User Experience in Recommender Systems

Over the years we have seen recommender systems shifting focus from optimizing short-term engagement toward improving long-term user experience on the platforms. While defining good long-term user experience is still an active research area, we focus on one specific aspect of improved long-term user experience here, which is user revisiting the platform. These long term outcomes however are much harder to optimize due to the sparsity in observing these events and low signal-to-noise ratio (weak connection) between these long-term outcomes and a single recommendation. To address these challenges, we propose to establish the association between these long-term outcomes and a set of more immediate term user behavior signals that can serve as surrogates for optimization. To this end, we conduct a large-scale study of user behavior logs on one of the largest industrial recommendation platforms serving billions of users. We study a broad set of sequential user behavior patterns and standardize a procedure to pinpoint the subset that has strong predictive power of the change in users' long-term visiting frequency. Specifically, they are predictive of users' increased visiting to the platform in $5$ months among the group of users with the same visiting frequency to begin with. We validate the identified subset of user behaviors by incorporating them as reward surrogates for long-term user experience in a reinforcement learning (RL) based recommender. Results from multiple live experiments on the industrial recommendation platform demonstrate the effectiveness of the proposed set of surrogates in improving long-term user experience.

• Data Mining and Modeling

• Machine Intelligence