Recommender System: Online Service Orchestration and Architecture

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By GarvinLi

In this article, Alibaba technical expert Aohai introduces the online service orchestration and architecture, specifically, the online inference service architecture and online multi-goal implementation.

1) Online Inference Service Architecture

To create the whole process of matching and ranking, we use Alibaba Cloud Container Service for Kubernetes (ACK) to build the entire inference architecture based on highly scalable and elastic business scenarios. The process includes the following steps: (1) Multi-channel matching: The inference module uses item-based collaborative filtering, semantic matching, hot matching, and operation strategy matching to retrieve thousands of candidates. (2) Exposed content deduplication: Based on users’ read history, the inference module removes the exposed content and the content that cannot be recommended based on the operation strategy. (3) Ranking: When the inference module calls the ranking process, it obtains user and item features based on user IDs and item IDs. It then calls the Elastic Algorithm Service (EAS) provided by Machine Learning Platform for AI (PAI) in batches to return the ranking results. In the following figure, the right part shows the online monitoring capability of the PAI-EAS online inference service. You need to dynamically scale out or scale in the ranking model based on its metric to avoid a high RT or that the QPS cannot meet your business requirements. This is the entire online service orchestration framework.

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2) Online Multi-goal Implementation

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Learn more about Alibaba Cloud Machine Learning Platform for AI (PAI) at https://www.alibabacloud.com/product/machine-learning

The views expressed herein are for reference only and don’t necessarily represent the official views of Alibaba Cloud.

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