Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

TL;DR

This paper introduces D$^2$Co, a method to correct watch time data by removing duration bias and noisy watching effects, improving the accuracy of user interest estimation in video recommendations.

Contribution

The paper presents a novel causal analysis of watch time and proposes a two-step correction method to better infer true user interest from biased and noisy watch time data.

Findings

D$^2$Co effectively reduces bias and noise in watch time data.

Experimental results show improved user interest estimation accuracy.

Online A/B tests demonstrate increased recommendation relevance.

Abstract

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but also by other factors, such as duration bias and noisy watching. Duration bias refers to the tendency for users to spend more time on videos with longer durations, regardless of their actual interest level. Noisy watching, on the other hand, describes users taking time to determine whether they like a video or not, which can result in users spending time watching videos they do not like. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for indicating user interest. Furthermore, current methods primarily address duration bias and ignore the impact of noisy watching, which may limit their effectiveness in uncovering user interest from watch time. In this study, we first analyze the generation mechanism of users' watch time from a unified causal viewpoint. Specifically, we considered the watch time as a mixture of the user's actual interest level, the duration-biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D$^2$Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets and online A/B testing indicate the effectiveness of the proposed method.