Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework

TL;DR

This paper introduces an on-device gating and ranking framework that detects potential playback issues in real-time and replaces problematic videos with locally cached ones, significantly improving user experience in video recommender systems.

Contribution

The paper presents a novel on-device framework that cooperates with server-side RS to enhance playback performance, deployed at scale on Kwai, improving user retention.

Findings

Significant reduction in playback issues during browsing.

Enhanced user experience and increased retention rates.

Successful large-scale deployment on Kwai platform.

Abstract

Video recommender systems (RSs) have gained increasing attention in recent years. Existing mainstream RSs focus on optimizing the matching function between users and items. However, we noticed that users frequently encounter playback issues such as slow loading or stuttering while browsing the videos, especially in weak network conditions, which will lead to a subpar browsing experience, and may cause users to leave, even when the video content and recommendations are superior. It is quite a serious issue, yet easily overlooked. To tackle this issue, we propose an on-device Gating and Ranking Framework (GRF) that cooperates with server-side RS. Specifically, we utilize a gate model to identify videos that may have playback issues in real-time, and then we employ a ranking model to select the optimal result from a locally-cached pool to replace the stuttering videos. Our solution has been fully deployed on Kwai, a large-scale short video platform with hundreds of millions of users globally. Moreover, it significantly enhances video playback performance and improves overall user experience and retention rates.