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Lee Richardson

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    Surrogate for Long-Term User Experience in Recommender Systems
    Can Xu
    Lisa Mijung Chung
    Mohit Sharma
    Qian Sun
    Sriraj Badam
    Yuyan Wang
    KDD 2022 (2022)
    Preview abstract 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. View details
    Values of Exploration in Recommender Systems
    Can Xu
    Elaine Le
    Mohit Sharma
    Su-Lin Wu
    Yuyan Wang
    RecSys (2021)
    Preview abstract Reinforcement Learning (RL) has been sought after to bring next-generation recommender systems to improve user experience on recommendation platforms. While the exploration-exploitation tradeoff is the foundation of RL research, the value of exploration in RL based recommender systems is less well understood. Exploration, commonly seen as a tool to reduce model uncertainty in regions with sparse user interaction/feedback, is believed to cost user experience in the short term while the indirect benefit of better model quality arrives at a later time. We on the other hand argue that recommender systems have inherent needs for exploration and exploration can improve user experience even in the more imminent term. We focus on understanding the role of exploration in changing different facets of recommendation quality that more directly impact user experience. To do that, we introduce a series of methods inspired by exploration research to increase exploration in a RL based recommender system, and study their effect on the end recommendation quality, more specifically, \emph{accuracy, diversity, novelty and serendipity}. We propose a set of metrics to measure RL based recommender systems in these four aspects and evaluate the impact of exploration induced methods against these metrics. In addition to the offline measurements, we conduct live experiments on an industrial recommendation platform serving billions of users to showcase the benefit of exploration. Moreover, we use user conversion as an indicator of the holistic long-term user experience and study the values of exploration in helping platforms convert users. Connecting the offline analyses and live experiments, we start building the connections between these four facets of recommendation quality toward long term user experience and identify serendipity as a desirable recommendation quality that changes user states and improves long term user experience. View details
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