Recommender Systems

1. Beginner guide vidhya

2. Real python on CF

3. Intro to, using item-item or user-item, validating using imdb data, git

4. Tfidf cosine similarity, countvec cosine

5. Various implementations of CF, a serious review of algorithms

6. Collaborative filtering, SVD

7. Part1, Spotlight, item2vec, Neural nets for Recommender systems

8. A general tutorial, has a nice intro

9. Medium on Movies

   1. Part 1 matrix factorization in movies, users vs movies.

   2. Part 2 using collaborative filtering using open ai

   3. Part 3 using col-filtering with neural nets

10. Medium series on collaborative filtering and embeddings Part 1, part 2, git

11. Movie recommender systems on kaggle

    1. On git

12. Matrix factorization

13. Collaborative filtering with binary countvec data, item-item, didnt work well on another domain

14. Netflix competition, matrix factorization over classical algorithms, a survey paper

15. Movie similarity based on genre

16. Similar entities, matrix multiplication high sparsity

17. Euclidean distance with high sparse data

18. Excel & fastai, git

19. CF for movie recommendation

20. Comparison item vs user cf

21. build a recommendation engine with collaborative filtering

Evaluating Recommender Systems

1. An exhaustive list of methods to evaluate

2. Choosing the best for your business

3. Evaluating

4. survey of accuracy eval metrics for RS by Microsoft

5. Building a validation framework

6. Evaluation Metrics for RS

7. offline vs online validation

8. Evaluating RS

TOOLS

1. Surprise, docs,

2. Grover prince , related article

3. Recsys git