MATH-80600A-Project Topic Undetermined!

Repository setup / Getting started

1. Clone this repository

git clone https://github.com/YichenLin/MATH-80600A-Project
cd MATH-80600A-Project

2. Install the required Python packages with pipenv:

pipenv install -r requirements.txt

3. Draft List of Kaggles for Recommender Systems in DL

https://www.kaggle.com/jieyima/netflix-recommendation-collaborative-filtering https://www.kaggle.com/c/avazu-ctr-prediction/data

https://towardsdatascience.com/building-a-recommendation-system-using-neural-network-embeddings-1ef92e5c80c9

https://www.kaggle.com/anupriyo/book-recommendation-system

4. Suggested types of recommender systems:

There are mainly 6 types of the recommendations systems :- Popularity based systems :- It works by recommeding items viewed and purchased by most people and are rated high.It is not a personalized recommendation. Classification model based:- It works by understanding the features of the user and applying the classification algorithm to decide whether the user is interested or not in the prodcut. Content based recommedations:- It is based on the information on the contents of the item rather than on the user opinions.The main idea is if the user likes an item then he or she will like the “other” similar item. Collaberative Filtering:- It is based on assumption that people like things similar to other things they like, and things that are liked by other people with similar taste. it is mainly of two types: a) User-User b) Item -Item Hybrid Approaches:- This system approach is to combine collaborative filtering, content-based filtering, and other approaches . Association rule mining :- Association rules capture the relationships between items based on their patterns of co-occurrence across transactions.

5. Suggested Articles to find the type of DL in Recommender systems:

https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-2-deep-recommendation-sequence-prediction-automl-f134bc79d66b

https://arxiv.org/pdf/1707.07435.pdf

https://towardsdatascience.com/recommendation-system-series-part-2-the-10-categories-of-deep-recommendation-systems-that-189d60287b58

What they do at Youtube:

Part 1: https://towardsdatascience.com/introduction-to-recommender-systems-1-971bd274f421

Part 2: https://towardsdatascience.com/introduction-to-recommender-systems-2-deep-neural-network-based-recommendation-systems-4e4484e64746

6. Misc:

https://analyticsindiamag.com/top-open-source-recommender-systems-in-python-for-your-ml-project/

7. Google Course:

Course: https://developers.google.com/machine-learning/recommendation

Colab: https://colab.research.google.com/github/google/eng-edu/blob/main/ml/recommendation-systems/recommendation-systems.ipynb?utm_source=ss-recommendation-systems&utm_campaign=colab-external&utm_medium=referral&utm_content=recommendation-systems#scrollTo=K7NJT9gbo4ub

8. Netflix Sides:

https://www.slideshare.net/AnoopDeoras/tutorial-on-deep-learning-in-recommender-system-lars-summer-school-2019

9. List of Datasets:

https://recommender-systems.com/resources/datasets/

https://cseweb.ucsd.edu/~jmcauley/datasets.html

https://github.com/caserec/Datasets-for-Recommender-Systems

10. Starting Point with Auto Encoders (REARRANGE)

https://towardsdatascience.com/recommendation-system-series-part-6-the-6-variants-of-autoencoders-for-collaborative-filtering-bd7b9eae2ec7