Provides the implementation and evaluation of a query-context-aware learning-to-rank model for sequential recommendation. The model enhances recommendation accuracy by incorporating the user's target query context into the ranking with the CLM modeling.

• UMAP '26 Short Research Paper Track: Harnessing Query Context for Personalized User Intent-Aware Sequential Recommendation
• Extended Version: arXiv
• CARS @ RecSys '25: workshop, presentation

├── datasets/                # Contains preprocessing notebooks
│   ├── {preprocessing notebooks for Taobao and RetailRocket}
├── files/                   # Stores processed dataset files
├── model.py                 # Model definition
├── train_model.py           # Script for model training
└── README.md                # Documentation

The notebooks in the datasets directory contain code to automatically download and preprocess the datasets. After running these notebooks, the processed training and test data will be saved as Parquet files in the files directory.

The entry point to start training the model is the train_model.py.

Parameters:

• --dataset: either taobao or retailrocket, to select the respective dataset
• --train_path: path to the local folder, storing the training datasets
• --test_path: path to the local folder, storing the training datasets
• --output_data_path: path for saving the resulting model artifact
• --integration: one of the following options (default: NO_QUERY_CONTEXT):
  • NO_QUERY_CONTEXT for no query context (the respective input will be ignored, and only the items sequence will be used)
  • OUTSIDE for query context outside the transformer blocks
  • IN_INPUT for implementation where query context is concatenated with the preceding item representation in the input
  • LAST_LAYER_AND_OUTSIDE for query context included in the last layer's query position and outside the transformer blocks
• --num_epochs: number of training epochs (default: 30)
• --num_samples: number of samples to use from dataset; use -1 for all samples (default: -1)
• --seq_len: maximum sequence length for input data (default: 100)
• --batch_size: batch size for training (default: 128)
• --past_query_context_in_test: whether to use past query context information during evaluation (0=only the current context is used, 1=historical context is used as well, default: 0)
• --query_context_dropout_rate: dropout rate for query context information; applicable only for IN_INPUT integration type (default: 0.0)
• --query_context_dropout_in_train: whether to apply query context dropout during training; applicable only for IN_INPUT integration typ (0=disabled, 1=enabled, default: 0)

Below are example commands for training the model with different integration types and options. Replace paths as needed.

Quick experiment with a small subset:

python train_model.py --dataset taobao --train_path datasets/files --test_path datasets/files --output_data_path ./model_output --integration IN_INPUT --num_epochs 2 --num_samples 10000 --batch_size 32

No query context (NO_QUERY_CONTEXT):

python train_model.py --dataset taobao --train_path datasets/files --test_path datasets/files --output_data_path ./model_output --integration NO_QUERY_CONTEXT

Query context outside transformer blocks (OUTSIDE):

python train_model.py --dataset taobao --train_path datasets/files --test_path datasets/files --output_data_path ./model_output --integration OUTSIDE

Query context concatenated in input (IN_INPUT):

python train_model.py --dataset taobao --train_path datasets/files --test_path datasets/files --output_data_path ./model_output --integration IN_INPUT

IN_INPUT with query context dropout (rate 0.25, enabled during training):

python train_model.py --dataset taobao --train_path datasets/files --test_path datasets/files --output_data_path ./model_output --integration IN_INPUT --query_context_dropout_rate 0.25 --query_context_dropout_in_train 1

IN_INPUT using historical query context during evaluation:

python train_model.py --dataset taobao --train_path datasets/files --test_path datasets/files --output_data_path ./model_output --integration IN_INPUT --past_query_context_in_test 1

Query context in last layer and outside transformer blocks (LAST_LAYER_AND_OUTSIDE):

python train_model.py --dataset taobao --train_path datasets/files --test_path datasets/files --output_data_path ./model_output --integration LAST_LAYER_AND_OUTSIDE