`GradREC`

NB: Repo is still WIP!

Link to Paper : https://aclanthology.org/2022.ecnlp-1.22/

Overview

Comparative recommendations answer questions of the form "Can I have something darker/longer/warmer". Most methods require extensive labelled data and employ learning-to-rank approaches. GradREC is a zero-shot approach toward generating comparative recommendations. We achieve this by framing comparative recommendations as latent space traversal -- a key component of our approach is leveraging the latent space learnt by FashionCLIP, a CLIP-like model fine-tuned for fashion concepts.

We postulated that like other self-supervised approaches for learning representations (Mikolov et al., 2012), the contrastive learning approach employed by CLIP-like models should also encode certain concepts (e.g. analogies) despite being unsupervised/self-supervised. Of particular interest is the idea that it should be possible to traverse the latent space in a manner whereby we can discover products that vary along a certain attribute/dimension of interest (e.g. shoes of increasing heel height).

Our preliminary investigation of this hypothesis established that we are indeed able to extract such comparative knowledge/concepts from CLIP-like models, allowing us to make comparative recommendations in a zero-shot fashion.

We provide in this repo our implementation of our method, and an interactive demo that explores how GradREC works.

API & Demo

Pre-requisites

To access the private bucket necessary to retrieve model weights and dataset, be sure to include an .env file containing the following:

AWS_ACCESS_KEY_ID
AWS_SECRET_KEY

Critically, GradREC builds upon the fashion-clip package found here.

GradREC API

The GradREC API provides an implementation of the latent space traversal approach described in our paper. It requires for initialization an instance of a FashionCLIP object (see fashion-clip repo). The API provides helper methods for each step in our methodology such as traversal vector construction and traversal function, with the ability to control the various parameters.

Usage

A GradREC object is initialized as follows,

from gradient_rec import GracREC

# we assume fclip is a FashionCLIP object 
gradrec = GradREC(fclip)

Several important methods are:

direction_vector(self, start_query: str, end_query: str, start_N=100, end_N=1000)
- start_query: text for starting point to generate the semantic difference vector
- end_query: text for ending point to generate the semantic difference vector
- start_N: number of products to retrieve for start query
- end_N: number of products to retrieve for end query
traverse_space(self, start_point: np.ndarray, search_space: np.ndarray, v_dir: np.ndarray, step_size: float, steps: int, reg_space: np.ndarray, reg_weight: float, reg_k: int = 100, k=10)
- start_point: initial seed product vector or any point in space
- search_space: latent representation of products to search
- v_dir: traversal vector
- step_size: size for each step taken
- steps: number of steps to take
- reg_space: latent representation of products to use for regularization
- reg_weight: weight for amount of regularization to use
- reg_k: number of nearest neighbors for use in regularization
- k: number of products to return for each traversal step

Please see the notebook inlcuded in this repo for a worked-out example of how to use GradREC!

GradREC Demo

The demo is built using streamlit, with further instructions and explanations included inside.

Running the app requires access to the dataset/fine-tuned model. See below for a preview of the demo. Stay tuned for more updates!

How to run

$ cd app
$ streamlit run app.py

Citation

@inproceedings{chia-etal-2022-come,
    title = "{``}Does it come in black?{''} {CLIP}-like models are zero-shot recommenders",
    author = "Chia, Patrick John  and
      Tagliabue, Jacopo  and
      Bianchi, Federico  and
      Greco, Ciro  and
      Goncalves, Diogo",
    booktitle = "Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.ecnlp-1.22",
    doi = "10.18653/v1/2022.ecnlp-1.22",
    pages = "191--198",
    abstract = "Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color? We consider item recommendations of the comparative nature (e.g. {``}something darker{''}) and show how CLIP-based models can support this use case in a zero-shot manner. Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.",
}

aliyeysides/gradient-recs

GradREC