Google Research

Deconfounding User Satisfaction Estimation from Response Rate Bias

Proceedings of the 14th ACM Conference on Recommender Systems (2020)


Improving user satisfaction is at the forefront of industrial recommender systems. While significant progress in recommender systems has relied on utilizing logged implicit data of user-item interactions (i.e., clicks, dwell/watch time, and other user engagement signals), there has been a recent surge of interest in measuring and modeling user satisfaction, as provided by orthogonal data sources. Such data sources typically originate from responses to user satisfaction surveys, which are explicitly asking users to rate their experience with the system and/or specific items they have consumed in the recent past. This data can be valuable for measuring and modeling the degree to which a user has had a satisfactory experience with the recommender, since what users do (engagement) does not always align with what users say they want (satisfaction as measured by surveys).

We focus on a large-scale industrial system trained on user survey responses to predict user satisfaction. The predictions of the satisfaction model for each user-item pair, combined with the predictions of the other models (e.g., engagement-focused ones), are fed into the ranking component of a real-world recommender system in deciding items to present to the user. It is therefore imperative that the satisfaction model does an equally good job on imputing user satisfaction across slices of users and items, as it would directly impact which items a user is exposed to. However, the data used for training satisfaction models is specifically biased in that users are more likely to respond to a survey when they will respond that they are more satisfied. When the satisfaction survey responses in slices of data with high response rate follow a different distribution than those with low response rate, response rate becomes a confounding factor for user satisfaction estimation.

We find a positive correlation between response rate and ratings in a large-scale survey dataset collected in our case study. To address this inherent response rate bias in the satisfaction data, we propose an inverse propensity weighting approach within a multi-task learning framework. We extend a simple feed-forward neural network architecture predicting user satisfaction to a shared-bottom multi-task learning architecture with two tasks: the user satisfaction estimation task, and the response rate estimation task. We concurrently train these two tasks, and use the inverse of the predictions of the response rate task as loss weights for the satisfaction task to address the response rate bias. We showcase that by doing this, (i) we can accurately model whether a user will respond to a survey, (ii) we improve the user satisfaction estimation error for the data slices with lower propensity to respond while not hurting that of the slices with higher propensity to respond, and (iii) we demonstrate in live A/B experiments that applying the resulting satisfaction predictions from this approach to rank recommendations translates to higher user satisfaction.

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