Integrate PAI-Rec with an existing system: Fine-grained sorting replacement - Platform For AI

If you have a mature recommendation system — one that already handles recall, filtering, fine-grained sorting, and re-sorting — you can replace only the fine-grained sorting and re-sorting stages with PAI-Rec, while keeping your existing recall results. Once the PAI-Rec fine-grained sorting model shows significant gains in experiments, you can gradually migrate your recall policies to the PAI-Rec engine.

Prerequisites

Before you begin, make sure you have:

PAI-FeatureStore configured with user and item features
A fine-grained sorting model deployed via EasyRec Processor (TensorFlow) or TorchEasyRec Processor (PyTorch)
Feature engineering and model deployment code generated and deployed to DataWorks

Architecture overview

Your existing system handles recall and filtering, then passes the candidate list to PAI-Rec. PAI-Rec performs fine-grained sorting and returns the ranked list to your system.

Recall and filtering: Your existing system runs recall and filtering, producing a candidate item list.
Traffic splitting and API calls: Split a portion of traffic by user ID or another method, then call the PAI-Rec recommendation engine API. Pass the candidate item list in the item_list parameter.
PAI-Rec fine-grained sorting: PAI-Rec uses item_list as its recall source. The engine retrieves user and item features, scores and sorts the items using the deployed fine-grained sorting model, and can apply re-sorting afterward.
Result return and logging: PAI-Rec returns the sorted item list along with the experiment ID (exp_id) and request ID (request_id). Record both values in your client:
- exp_id: identifies which experiment the result belongs to — required for experiment attribution and performance analysis
- request_id: uniquely identifies the request — required for data diagnostics and troubleshooting

PAI-Rec configuration

Configure the following in your PAI-Rec solution, which uses PAI-FeatureStore to manage features.

Model scoring service

Choose the processor that matches your model framework:

Framework	Processor	Reference
TensorFlow	EasyRec Processor	EasyRec Processor
PyTorch	TorchEasyRec Processor	TorchEasyRec Processor

PAI-Rec engine settings

Configure the following in the PAI-Rec engine:

User features: Retrieve user features from FeatureStore. See PAI-FeatureStore feature configuration.
ContextItemRecall: Configure Context Item Recall (ContextItemRecall) in the engine configuration. After this feature is configured, the engine uses the item_list passed through the API as its recall source.
```
{
    "SceneConfs": {
        "${scene_name}": {
            "default": {
                "RecallNames": [
                    "ContextItemRecall"
                ]
            }
        }
    }
}
```
Fine-grained sorting model: Configure the fine-grained sorting model call in the engine configuration file. See Fine-grained sorting configuration.
A/B testing: Use the A/B Test service to manage internal A/B experiments. Set up multiple sorting models using bucketing by user ID.

User-side changes

Your existing system retains all recall, filtering, and exposure operations. Make the following two changes to integrate with PAI-Rec:

1. Call the PAI-Rec engine API

Modify your recommendation request logic to call the PAI-Rec engine API. In the request, pass:

item_list: item IDs from your recall and filtering stages
User ID and user features
Filtering rules

2. Record log fields

In your exposure and behavior logs, record the following fields from each PAI-Rec API response:

Field	Purpose	Impact if missing
`exp_id`	Identifies the experiment — used for experiment attribution and performance analysis	Cannot attribute traffic or evaluate experiment results
`request_id`	Uniquely identifies the request — used for data diagnostics and troubleshooting	Cannot trace or diagnose data issues after the fact