https://github.com/cair/vibrent_clothes_rental_dataset_collection

https://github.com/cair/vibrent_clothes_rental_dataset_collection

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• Host: GitHub
• URL: https://github.com/cair/vibrent_clothes_rental_dataset_collection
• Owner: cair
• Created: 2024-08-16T13:33:43.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-08-16T13:40:00.000Z (8 months ago)
• Last Synced: 2025-02-04T21:45:09.880Z (3 months ago)
• Language: Jupyter Notebook
• Size: 9.07 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Clothes Rental Dataset Collection

![Collage of dataset pictures](reports/figures/collage.jpg)

Repository for assorted scripts collecting and processing the clothes rental dataset.

The dataset will be made available for download at: https://www.kaggle.com/datasets/kaborg15/vibrent-clothes-rental-dataset

Baseline results are calculated in the notebooks:

notebooks/evaluate_dataset_baselines.ipynb

notebooks/evaluate_dataset_baselines_content_based.ipynb

notebooks/evaluate_dataset_baselines_matrix_factorization.ipynb

## A Brief Descrioption of Baseline methods:

### Heuristic Baselines

- **Popular Outfits (Pop)**: Recommends the n globally most commonly rented outfits to every single user.

- **Repeat Outfits (Rep)**: Recommends the n outfits the user has previously rented.

- **Popular and Repeated Outfits (Pop + Rep)**: A combination of Popular Outfits and Repeat Outfits. Recommends repeated items if the user has less than n rentals. Otherwise, the recommendations are padded with the most popular global items.

### Collaborative Filtering Baselines

- **Alternating Least Squares (ALS)**: ALS is a matrix factorization technique particularly effective for handling large-scale and sparse datasets and can incorporate both explicit and implicit feedback [1]. ALS has been applied to our data with 32 factors and regularization of 0.01.

- **Bayesian Personalized Ranking (BPR)**: BPR is a pairwise learning method designed to optimize for personalized ranking. It utilizes a matrix factorization model with the objective of maximizing the difference between the observed positive interactions and unobserved negative interactions [2]. BPR has been applied to our dataset with 128 factors, a regularization of 0.01, and a learning rate of 0.01.

### Content-based Baselines

- **Image Embeddings (Img Embed)**: A content-based approach built on the Vibrent Clothes Rental dataset image embeddings. Recommendations are made by calculating the n nearest neighbors of a mean representation of all images in a user's rental history.

- **Tag Embeddings (Tag Embed)**: A content-based approach built on each outfit's tags. Each outfit's set of tags is one-hot encoded, and a user is represented as the mean embeddings across its rental history. Recommendations are made by calculating the n nearest neighbor outfit embeddings of these user embeddings.