Related papers: Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks

Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks

URL: http://arxiv.org/abs/2312.04071v1
Date: Thu, 7 Dec 2023 06:29:26 GMT
Title: Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Authors: Zijie Huang, Baolin Li, Hafez Asgharzadeh, Anne Cocos, Lingyi Liu, Evan Cox, Colby Wise, Sudarshan Lamkhede
Abstract summary: We study the problem in the context recommender systems at Netflix. We propose a novel graph-based approach called SemanticGNN. Our key technical contributions are twofold: (1) we develop a novel relation-aware attention graph neural network (GNN) to handle the imbalanced distribution of relation types in our graph; (2) to handle web-scale graph data that has millions of nodes and billions of edges, we develop a novel distributed graph training paradigm.
Score: 4.261438296177923
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by collaborative filtering approaches, i.e. if users co-engage with a pair of entities frequently enough, the embeddings should be similar. However, relying on co-engagement data alone can result in lower-quality embeddings for new and unpopular entities. We study this problem in the context recommender systems at Netflix. We observe that there is abundant semantic information such as genre, content maturity level, themes, etc. that complements co-engagement signals and provides interpretability in similarity models. To learn entity similarities from both data sources holistically, we propose a novel graph-based approach called SemanticGNN. SemanticGNN models entities, semantic concepts, collaborative edges, and semantic edges within a large-scale knowledge graph and conducts representation learning over it. Our key technical contributions are twofold: (1) we develop a novel relation-aware attention graph neural network (GNN) to handle the imbalanced distribution of relation types in our graph; (2) to handle web-scale graph data that has millions of nodes and billions of edges, we develop a novel distributed graph training paradigm. The proposed model is successfully deployed within Netflix and empirical experiments indicate it yields up to 35% improvement in performance on similarity judgment tasks.

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