Recommender System Architecture Recommende r System for Service Recommender System for Service Recommender System for Service Recommender System for Service Recommended Items (Candidates) Ranker Trainer Events (imp, click, etc) LINE App User ID Items Model parameter Item Request Top k items for each user
Ranker Item A 0.7 Current Expected Score 0.4 Current Expected Score 0.6 Current Expected Score 0.1 Current Expected Score Item B Item C Item D > Ranker chooses an item from candidates A, B, C … by using contextual bandits > Each expected score is computed by a prediction model corresponding to the item
Prediction Model > Imp: 0.5, Click: 1.0, Mute: 0.0 > Balance Exploration-Exploitation Tradeoff > Laplace Approximation Bayesian Factorization Machine (FM) as an Arm of Contextual Bandits Output User ID Item ID User Features (Gender, Age, …) Other Features (Timestamp, …) Bayesian FM Embedding Embedding
Parameter Server for Distributed ML Events LINE App Trainer Worker Model Worker Model Parameter Server Ranker Executor Model Executor Model Δw W W Request Contents
Example of asynchronous communications between the parameter server and trainers. In the situation, learning doesn't work well just by accumulating the gradient in the parameter server. Asynchronous Distributed Online Learning
Asynchronous distributed learning algorithm Example of asynchronous communications between the parameter server and trainers. In the situation, learning doesn't work well just by accumulating the gradient in the parameter server. Asynchronous Distributed Online Learning Deceleration Backtrack
Primary Performance Metric > Consistent with user satisfaction trends obtained from questionnaire research > Easy to calculate > Stable under temporary fluctuations due to user's unfamiliarity Why score is used as main indicator?
Primary Performance Metric > Consistent with user satisfaction trends obtained from questionnaire research > Easy to calculate > Stable under temporary fluctuations due to user's unfamiliarity Why score is used as main indicator? Release new types of contents, or expand target users
Offline Test Off-policy Evaluation We use the More Robust Doubly Robust (MRDR) algorithm to estimate the performance of a new logic from the data generated by other logics. Framework of Offline Test To Evaluate New Logic Offline Test Environment Parameter server and trainers are clones of the production system. We use the event logs stored in DataLake by using PySpark. Trainer Parameter Server (Offline) Ranker DataLake
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