Recommender System: Ranking Algorithms and Training Architectures

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By GarvinLi

In this article, Alibaba technical expert Aohai introduces the ranking algorithms and training architectures of a recommender system, specifically, the introduction of the ranking module in a recommender system, ranking algorithms, and online and offline training architectures for a ranking model.

1) The Ranking Module in a Recommender System

Specifically, the matching module first selects a small proportion of items that the user may like from a huge number of items. For example, the platform has 100,000 items, and the matching module filters out 500 items that the user may like. Then, the ranking module ranks these items based on the user’s preferences. To rank the 500 items from the user’s favorite to the least favorite, we need a ranking algorithm, which can provide a rank of the items based on user and item properties.

2) An Introduction to Ranking Algorithms

3) Offline Training Architecture of a Ranking Model

Offline training uses T — 1 data. Specifically, the models used for businesses today are trained based on data from before today. Offline training allows you to integrate a large amount of historical data to one data warehouse, train a model based on features and data of all samples, and verify the trained model offline. You can then use the verified model online the next day, which ensures security and good performance. The following figure provides an offline model training architecture. Currently, almost all customers train ranking models offline. If you want to perform real-time training, you will face great challenges in the architecture.

4) Online Training Architecture of a Ranking Model

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

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