Bootstrapping Recommendations at Chrome Web Store
We describe how we built three recommendation products from scratch at Google Chrome Web Store, namely context-based recommendations, related extension recommendations, and personalized recommendations. Unlike most existing papers that focus on novel algorithms, this paper focuses on sharing practical experiences building large scale recommender systems under various real-world constraints, such as privacy constraints, data sparsity issues, highly skewed data distribution, and product design choices, such as user interface. We show how these constraints make standard approaches difficult to succeed in practice. We share success stories that turn very negative live metrics to very positive, by introducing 1) how we use interpretable neural models to bootstrap the systems, helps identifying pipeline issues, and paves way for more advanced models. 2) A new item-item based recommendation algorithm that works under highly skewed data distributions, and 3) how two products can help bootstrapping the third one, which significantly reduces development cycles and bypasses various real-world difficulties. All the explorations in this work are verified in live traffic on millions of users. We believe the findings in this work can help practitioners to bootstrap and build large-scale recommender systems.