Using machine learning to predict what file you need next, Part 2

This post details the improvements made to Dropbox's content suggestion system, focusing on handling diverse content types and enhancing ML models. Key contributions include: 1. Developing file extension embeddings trained via a weakly-supervised task to handle heavy-tailed distributions and semantically similar extensions. 2. Incorporating filenames as a signal using char-RNNs for better semantic understanding. 3. Training separate models for cloud-based documents and Paper docs due to data limitations, merging results with a combiner model. 4. Decoupling file and folder suggestions, reusing pipelines but adapting candidate generation, signal fetching, and training data definitions for folders. 5. Introducing dbxlearn, an ML infrastructure tool for elastic computing and advanced hyperparameter tuning (Bayesian Optimization), enabling faster iteration and experimentation.