faizanahemad / hybrid-weighted-embedding-recommender

A Hybrid Recommendation system which uses Content embeddings and augments them with collaborative features. Weighted Combination of embeddings enables solving cold start with fast training and serving

Hybrid-Weighted-Embedding-Recommendation

A Hybrid Recommendation system which uses Content embeddings and augments them with collaborative features. Weighted Combination of embeddings enables solving cold start with fast training and serving

TODO:

• Improve docs
• Multi-item types
• Users-Multiple_item_types-Other-things enabled graph
• No users and items just unique str ids
• de-couple affinity vectors from rating vectors
• Proper example of how to build and test an external dataset with ML-100K
  • Example of using content data and not using it
• Make system independent of content so recsys with no content can be used.
• Make a section in readme of how to reproduce
• From Factorization meets the neighborhood paper take section 6 - evaluation of a top-K recommender and implement its metric system
• Priming of GCN vectors can be done by unbiased svd instead of word2vec
• Positive, Negative and Anchor can be weighed separately
• Validation module, try predicting link prediction accuracy by taking test links and mixing fake links in same proportion

TODO:

• Paper: Figure out sections and relevant papers to take content arrangement hints.

• Figure out latex template codes for relevant conferences.

  • AMLC
  • ICLR/AAAI/NIPS/ICML/IEEE/ACM kdd, sigkdd, recsys

• ML-20M

• ML-100K/ML-1M/ML-20M vanilla, feat, text-feat

• Ablation Study

  • Resnet Arch vs Normal Arch
  • Text Features, other features, no features/ no content
  • No/Gaussian noise
  • Node2vec, triplet vectors input

Environment Setup

• Install Anaconda from https://www.anaconda.com/distribution/

Add .condarc to your home dir with below contents

auto_update_conda: False
channels:
  - defaults
  - anaconda
  - conda-forge
always_yes: True
add_pip_as_python_dependency: True
use_pip: True
create_default_packages:
  - pip
  - ipython
  - jupyter
  - nb_conda
  - setuptools
  - wheel

conda update conda

conda create -n hybrid-recsys python=3.7.4

conda activate hybrid-recsys

Install Fasttext

wget https://github.com/facebookresearch/fastText/archive/v0.9.1.zip
unzip v0.9.1.zip
cd fastText-0.9.1 && make -j4 && pip install .

Install Tensorflow 2.0 from here

pip install --upgrade pip
pip install tensorflow

pip install -r requirements.txt

Experiments

• Content Based
• Content + Collaborative with extra features
• Content + Collaborative with extra features with alpha tree

TODO

• Add new item/user with content features https://stats.stackexchange.com/questions/320962/matrix-factorization-in-recommender-systems-adding-a-new-user?rq=1
• Add Testing for Implicit and content data framework like https://github.com/benfred/implicit and https://github.com/lyst/lightfm and https://maciejkula.github.io/spotlight/index.html
• Add Testing for Fast.ai recommendation framework
• Add Recall@K for user's with less than L ratings in training.
• Add support to detect if gpu present and change env_setup.sh accordingly.
• Add Filter Function support for search functions
• Try Huber Loss
• Ability to Test with multiple recsys with different hyper-params
• Hyper param optimisation via bayesian opt
• Add Embedding layer to GCN Triplet training, see if time increases from 50s [Done]
• Try Normal triplet + GCN
• Image Auto-Encoder (De-noising) File based, Image Auto-Encoder DVAE. Use Average Rating for that item as a target too.
• In predict method, model.predict_generator instead of tfds for speed

Innovation

• Heterogenous Features via Deep Networks

• Weighted Triplet Loss

• Embedding Compression

  • We train in a higher Dimensional Space, After training we use autoencoders to reduce dimensionality.
  • Since our task involves cosine distance, after auto-enc step we do another step where we use triplet loss with Distances calculated from initial bigger embeddings. This is similar to TSNE.
  • the two steps can be combined into one encoder-decoder-triplet architecture where decoder loss and triplet loss are weighted and added.

• Combine Collaborative and Content Based Approach by

  • building content embeddings first
  • enhancing them with collaborative relations
  • Balancing between them using a weighted scheme to solve cold start problem

• Multiple hybrid embeddings for sellers at different life-cycle stages. Multiple alpha

References

Interesting Papers

• Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
• Inductive Representation Learning on Large Graphs

Datasets and Downloads

• https://github.com/celiao/tmdbsimple/
• https://www.kaggle.com/tmdb/tmdb-movie-metadata#tmdb_5000_movies.csv
• http://www.cs.cmu.edu/~ark/personas/ and https://github.com/askmeegs/movies
• https://www.kaggle.com/jrobischon/wikipedia-movie-plots/data#
• https://github.com/markriedl/WikiPlots
• https://www.kaggle.com/c/yelp-recsys-2013/data
• https://www.kaggle.com/rounakbanik/the-movies-dataset or Google Drive Mirror

Misc References

• http://stevehanov.ca/blog/?id=145
• https://towardsdatascience.com/lossless-triplet-loss-7e932f990b24
• http://fastml.com/evaluating-recommender-systems/

Triplet Loss

• https://www.tensorflow.org/addons/tutorials/losses_triplet
• https://github.com/noelcodella/tripletloss-keras-tensorflow/blob/master/tripletloss.py
• https://github.com/AdrianUng/keras-triplet-loss-mnist/blob/master/Triplet_loss_KERAS_semi_hard_from_TF.ipynb
• keras-team/keras#9498
• https://github.com/maciejkula/triplet_recommendations_keras
• https://towardsdatascience.com/lossless-triplet-loss-7e932f990b24

Dimensionality Reduction

• https://github.com/DmitryUlyanov/Multicore-TSNE
• https://github.com/lmcinnes/umap
• https://github.com/KlugerLab/FIt-SNE https://pypi.org/project/fitsne/0.1.10/
• https://stats.stackexchange.com/questions/402668/intuitive-explanation-of-how-umap-works-compared-to-t-sne
• https://github.com/nmslib/hnswlib

Metrics

• https://stackoverflow.com/questions/34252298/why-rank-based-recommendation-use-ndcg

Trouble-Shooting

• https://medium.com/@HojjatA/could-not-find-valid-device-for-node-while-eagerly-executing-8f2ff588d1e

Tools

• https://docs.aws.amazon.com/codecommit/latest/userguide/setting-up-gc.html
• https://github.com/eliorc/node2vec

Code

• https://github.com/lorenzMuller/kernelNet_MovieLens/blob/master/dataLoader.py

hybrid-weighted-embedding-recommender's Issues

Make dataset as Yelp2018.

Hi, I have another dataset and how can I create the same dataset as yelp2018? Can you help me!