Recommending What Video to Watch Next: A Multitask Ranking System

“In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform. The system faces many real-world challenges, including the presence of multiple competing ranking objectives, as well as implicit selection biases in user feedback. To tackle these challenges, we explored a variety of soft-parameter sharing techniques such as Multi-gate Mixture-of-Experts so as to efficiently optimize for multiple ranking objectives. Additionally, we mitigated the selection biases by adopting a Wide & Deep framework. We demonstrated that our proposed techniques can lead to substantial improvements on recommendation quality on one of the world’s largest video sharing platforms.”

“In this paper, we describe a large-scale ranking system for video recommendation. That is, given a video which a user is currently watching, recommend the next video that the user might watch and enjoy. Typical recommendation systems follow a two-stage design with a candidate generation and a ranking [10, 20]. This paper focuses on the ranking stage. In this stage, the recommender has a few hundred candidates retrieved from the candidate generation (e.g. matrix factorization [45] or neural models [25]), and applies a sophisticated large-capacity model to rank and sort the most promising items. We present experiments and lessons learned from building such a ranking system on a large-scale industrial video publishing and sharing platform.

Designing and developing a real-world large-scale video recommendation system is full of challenges, including:

• There are often different and sometimes conflicting objectives which we want to optimize for. For example, we may want to recommend videos that users rate highly and share with their friends, in addition to watching.

• There is often implicit bias in the system. For example, a user might have clicked and watched a video simply because it was being ranked high, not because it was the one that the user liked the most. Therefore, models trained using data generated from the current system will be biased, causing a feedback loop effect [33]. How to effectively and efficiently learn to reduce such biases is an open question.

To address these challenges, we propose an efficient multitask neural network architecture for the ranking system, as shown in Figure 1. It extends the Wide & Deep [9] model architecture by adopting Multi-gate Mixture-of-Experts (MMoE) [30] for multitask learning. In addition, it introduces a shallow tower to model and remove selection bias. We apply the architecture to video recommendation as a case study: given what user currently is watching, recommend the next video to watch. We present experiments of our proposed ranking system on an industrial large-scale video publishing and sharing platform. Experimental results show significant improvements of our proposed system.

Specifically, we first group our multiple objectives into two categories: 1) engagement objectives, such as user clicks, and degree of engagement with recommended videos; 2) satisfaction objectives, such as user liking a video on YouTube, and leaving a rating on the recommendation. To learn and estimate multiple types of user behaviors, we use MMoE to automatically learn parameters to share across potentially conflicting objectives. The Mixture-of-Experts [21] architecture modularizes input layer into experts, each of which focuses on different aspects of input. This improves the representation learned from complicated feature space generated from multiple modalities. Then by utilizing multiple gating networks, each of the objectives can choose experts to share or not share with others.”