# Recommender Systems 1. [Beginner guide](https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/) vidhya 2. [Real python on CF](https://realpython.com/build-recommendation-engine-collaborative-filtering/#steps-involved-in-collaborative-filtering) 3. [Intro to, using item-item or user-item](https://www.ethanrosenthal.com/2015/11/02/intro-to-collaborative-filtering/), validating using imdb data, git 4. [Tfidf cosine similarity](https://towardsdatascience.com/recommender-engine-under-the-hood-7869d5eab072), [countvec cosine](https://www.datacamp.com/community/tutorials/recommender-systems-python) 5. [Various implementations of CF](https://towardsdatascience.com/various-implementations-of-collaborative-filtering-100385c6dfe0), a serious review of algorithms 6. [Collaborative filtering, SVD](https://hackernoon.com/introduction-to-recommender-system-part-1-collaborative-filtering-singular-value-decomposition-44c9659c5e75) 7. [Part1,](https://hackernoon.com/introduction-to-recommender-system-part-1-collaborative-filtering-singular-value-decomposition-44c9659c5e75) [Spotlight, item2vec, Neural nets for Recommender systems](https://towardsdatascience.com/introduction-to-recommender-system-part-2-adoption-of-neural-network-831972c4cbf7) 8. [A general tutorial, has a nice intro](https://www.datacamp.com/community/tutorials/recommender-systems-python) 9. Medium on Movies 1. Part 1 [matrix factorization in movies, users vs movies.](https://towardsdatascience.com/fast-ai-season-1-episode-5-1-movie-recommendation-using-fastai-a53ed8e41269)[ ](https://towardsdatascience.com/fast-ai-season-1-episode-5-2-collaborative-filtering-from-scratch-1877640f514a) 2. [Part 2 using collaborative filtering](https://towardsdatascience.com/fast-ai-season-1-episode-5-2-collaborative-filtering-from-scratch-1877640f514a) using open ai 3. [Part 3 using col-filtering with neural nets](https://towardsdatascience.com/fast-ai-season-1-episode-5-3-collaborative-filtering-using-neural-network-48e49d7f9b36) 10. Medium series on collaborative filtering and embeddings [Part 1](https://towardsdatascience.com/collaborative-filtering-and-embeddings-part-1-63b00b9739ce), [part 2](https://towardsdatascience.com/collaborative-filtering-and-embeddings-part-2-919da17ecefb), [git](https://github.com/shik3519/collaborative-filtering) 11. [Movie recommender systems](https://www.kaggle.com/rounakbanik/movie-recommender-systems) on kaggle 1. [On git](https://github.com/jaypatel00174/Movie-Recommendation) 12. [Matrix factorization ](https://towardsdatascience.com/paper-summary-matrix-factorization-techniques-for-recommender-systems-82d1a7ace74) 13. [Collaborative filtering with binary countvec data, item-item, didnt work well on another domain](https://medium.com/radon-dev/item-item-collaborative-filtering-with-binary-or-unary-data-e8f0b465b2c3) 14. [Netflix competition, matrix factorization over classical algorithms, a survey paper](https://towardsdatascience.com/paper-summary-matrix-factorization-techniques-for-recommender-systems-82d1a7ace74) 15. [Movie similarity based on genre ](https://towardsdatascience.com/content-based-recommender-systems-28a1dbd858f5) 16. [Similar entities, matrix multiplication](https://medium.com/wbaa/https-medium-com-ingwbaa-boosting-selection-of-the-most-similar-entities-in-large-scale-datasets-450b3242e618) high sparsity 17. [Euclidean distance with high sparse data](https://stats.stackexchange.com/questions/117354/euclidean-distance-with-sparse-and-high-dimension-data) 18. Excel & fastai, [git](https://github.com/shik3519/collaborative-filtering/blob/master/cf-scratch-movielens/collaborative%20filtering%20from%20scratch.ipynb) 19. [CF for movie recommendation](https://medium.com/@wwwbbb8510/python-implementation-of-baseline-item-based-collaborative-filtering-2ba7c8960590) 20. [Comparison item vs user cf](https://medium.com/@wwwbbb8510/comparison-of-user-based-and-item-based-collaborative-filtering-f58a1c8a3f1d) 21. [build a recommendation engine with collaborative filtering](https://realpython.com/build-recommendation-engine-collaborative-filtering/) ## Evaluating Recommender Systems 1. [An exhaustive list of methods to evaluate](https://towardsdatascience.com/an-exhaustive-list-of-methods-to-evaluate-recommender-systems-a70c05e121de) 2. [Choosing the best for your business](https://medium.com/recombee-blog/evaluating-recommender-systems-choosing-the-best-one-for-your-business-c688ab781a35) 3. [Evaluating](https://medium.com/the-owl/evaluating-recommender-systems-749570354976) 4. [survey of accuracy eval metrics for RS by Microsoft](https://www.jmlr.org/papers/volume10/gunawardana09a/gunawardana09a.pdf) 5. [Building a validation framework](https://medium.com/moosend-engineering-data-science/building-a-validation-framework-for-recommender-systems-a-quest-ec173a24b56f) 6. [Evaluation Metrics for RS](https://towardsdatascience.com/evaluation-metrics-for-recommender-systems-df56c6611093) 7. [offline vs online validation](https://www.quora.com/How-do-I-validate-my-recommendation-system-without-prior-user-interaction-data) 8. [Evaluating RS](https://tzin.bgu.ac.il/~shanigu/Publications/EvaluationMetrics.17.pdf) ## **TOOLS** 1. [**Surprise**](https://github.com/NicolasHug/Surprise)**,** [**docs**](https://surprise.readthedocs.io/en/stable/FAQ.html#how-to-get-the-top-n-recommendations-for-each-user)**,** 2. [**Grover prince**](https://github.com/groverpr/Machine-Learning) **,** [**related article**](https://towardsdatascience.com/various-implementations-of-collaborative-filtering-100385c6dfe0) 3. [**Recsys**](https://github.com/ocelma/python-recsys) **git** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.mlcompendium.com/machine-learning/recommender-systems.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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