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fashion-image-recommender

Image Recommendation via similarity retrieval through deep learning

https://github.com/prakashsellathurai/fashion-image-recommender

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
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    Scientific vocabulary similarity
    Low similarity (3.8%) to scientific vocabulary

Keywords

annoy collaborative-filtering deep-learning knn recommendation-engine recommendation-system recommender-system similarity-retreivel
Last synced: 6 months ago · JSON representation ·

Repository

Image Recommendation via similarity retrieval through deep learning

Basic Info
  • Host: GitHub
  • Owner: prakashsellathurai
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 772 KB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 0
  • Open Issues: 5
  • Releases: 1
Topics
annoy collaborative-filtering deep-learning knn recommendation-engine recommendation-system recommender-system similarity-retreivel
Created over 5 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md

Fashion Image Recommender

Architecture

Model: VGG16 Xception

Data set: Amazon Image recommender dataset link.

Product Category Prediction Task: Xception model with transfered learning.

k-NN indexer: Annoy library by spotify link.

k-NN engine for similarity retrieval: the k-NN engine will retrieve the nearest 30 feature vectors and their product id in the predicted product category through similarity retrieval

Citation:

``` Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering R. He, J. McAuley WWW, 2016 (http://cseweb.ucsd.edu/~jmcauley/pdfs/www16a.pdf)

Image-based recommendations on styles and substitutes J. McAuley, C. Targett, J. Shi, A. van den Hengel SIGIR, 2015 (http://cseweb.ucsd.edu/~jmcauley/pdfs/sigir15.pdf)

Image-based Product Recommendation System with Convolutional Neural Networks Luyang Chen, Fan Yang, Heqing Yang CS231n, 2017 (http://cs231n.stanford.edu/reports/2017/pdfs/105.pdf) ```

Owner

  • Name: Prakash Sellathurai
  • Login: prakashsellathurai
  • Kind: user
  • Location: India
  • Company: Amazon

Software Engineer

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "prakashsellathurai"
  given-names: "prakash"
title: "fashion-Image-Recommender"
version: 1.0.0
date-released: 2021-07-31
url: "https://github.com/prakashsellathurai/fashion-Image-Recommender"

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Dependencies

app-engine-function/requirements.txt pypi
  • Click ==7.0
  • Flask ==1.1.1
  • Jinja2 ==2.11.3
  • Keras-Applications ==1.0.8
  • Keras-Preprocessing ==1.1.0
  • Markdown ==3.1.1
  • MarkupSafe ==1.1.1
  • Werkzeug ==0.15.5
  • absl-py ==0.8.0
  • annoy ==1.16.0
  • astor ==0.8.0
  • cachetools ==3.1.1
  • certifi ==2019.6.16
  • chardet ==3.0.4
  • gast ==0.2.2
  • google-api-core ==1.14.2
  • google-auth ==1.6.3
  • google-cloud-core ==1.0.3
  • google-cloud-storage ==1.19.0
  • google-pasta ==0.1.7
  • google-resumable-media ==0.3.3
  • googleapis-common-protos ==1.6.0
  • grpcio ==1.23.0
  • h5py ==2.9.0
  • idna ==2.8
  • itsdangerous ==1.1.0
  • numpy ==1.17.1
  • protobuf ==3.9.1
  • pyasn1 ==0.4.7
  • pyasn1-modules ==0.2.6
  • pytz ==2019.2
  • requests ==2.22.0
  • rsa ==4.7
  • six ==1.12.0
  • tb-nightly ==1.14.0a20190603
  • tensorflow ==2.5.2
  • termcolor ==1.1.0
  • tf-estimator-nightly ==1.14.0.dev2019060501
  • urllib3 ==1.26.5
server/requirements.txt pypi
  • Jinja2 ==2.11.3
  • firebase-admin ==2.14.0
  • flask ==1.1.1
  • google-cloud-firestore ==0.30.0
  • google-cloud-storage ==1.13.0
  • grpcio ==1.23.0