An (unofficial) PyTorch implementation of SizeFitNet (SFNet) architecture proposed in the paper A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce by Sheikh et. al (RecSys'19).

The original paper demonstrates experiments on two datasets:

1. ModCloth
2. RentTheRunWay

Both datasets are curated from fashion e-commerce websites that provide transaction information that contain customer attributes, article attributes and fit (target varible). fit is a categorical variable with small, fit and large as the possible labels.

The model consits of two pathways: one that captures user embeddings + user features, the other that captures item embeddings + item features. See the following figure taken from paper:

The representations within each pathway is transformed using residual/skip connections. The authors compare it against an MLP (without skip connections) baseline and show better performance.

Different from the paper, I combine the user representation (u) and item representation (v) into a new tensor as below:

   [u, v, |u-v|, u*v]

• concatenation of the two representations
• element-wise product u ∗ v
• absolute element-wise difference |u-v|

Based on: https://arxiv.org/pdf/1705.02364.pdf

This new representation is fed to the top layer skip connection block.

1. Start by installing the necessary packages (preferably within a Python virtual environment):

   pip install -r requirements.txt

2. Download the data from here and place it in the data/ directory.

3. The original data is quite messy with quite a bit of missing values (NaNs). As such, I dropped some attributes and did some data imputation. Refer to the data_exploration.ipynb notebook to understand the data processing steps. The notebook also provides instruction on creating the train/validation/test splits.

4. Set data, model and training configurations by appropriately modifying the jsonnet files under configs/ directory.

5. Train the SFNet Model:

   python train.py

The model checkpoints and run configuration will be saved under runs/<experiment_name> The above also generates tensorboard plots of training loss and validation metrics, that would be useful to view training progress.

6. Test the Model:

   python test.py --saved_model_path `runs/<experiment_name>`

The model was quite sensitive to hyperparameters. For certain sets of hyperparameters, the optimization process would diverge and the validation loss would shoot up. The configuration provided under configs/model.jsonnet was what worked best for me.

Below are some tensorboard graphs for validation metrics.

Note: The metrics reported here are macro-average values.

Some future work for this repository and ideas are plausible ways to improve the results:

• Experiments on RentTheRunWay Dataset
• L2-regularization on the embeddings
• Batch Normalization on the feed forward layers
• Early Stopping
• Learning Rate Decay
• Weighted Loss Function to account for the class-imbalance
• Contrastive Learning (Siamese Networks-?) to encourage learning different sub-spaces for positive and negative size-fits
• Topic Modelling Approaches
• Modelling Users/Items as Distributions

Thanks to Rishab Mishra for making the datasets used here publicly available on Kaggle. Some ideas for pre-processing the data were borrowed from NeverInAsh.