Update README.md

Author: Jamell Dacon

README.md

@@ -138,7 +138,104 @@ The following datasets are very popular in **Recommender Systems**, below are al
   
   > - Content: Part of the data has been first collected using the Kaggle API to retrieve the full list datasets, then each URL reference has been leveraged with a Python script in order to retrieve more detailed information.
   
-
+ 
+ 
+# A collection of resources for Recommender Systems (RecSys)
+
+## Recommendation Algorithms
+
+- Recommender Systems Basics
+  - [Wikipedia](https://en.wikipedia.org/wiki/Recommender_system)
+- Nearest Neighbor Search
+  - [Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
+  - [sklearn.neighbors](http://scikit-learn.org/stable/modules/neighbors.html)
+  - [Benchmarks of approximate nearest neighbor libraries](https://github.com/erikbern/ann-benchmarks)
+- Classic Matrix Facotirzation
+  - [Matrix Factorization: A Simple Tutorial and Implementation in Python](http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/)
+  - [Matrix Factorization Techiques for Recommendaion Systems](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf)
+- Singular Value Decomposition (SVD)
+  - [Wikipedia](https://en.wikipedia.org/wiki/Singular-value_decomposition)
+- SVD++
+  - [Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model](http://www.cs.rochester.edu/twiki/pub/Main/HarpSeminar/Factorization_Meets_the_Neighborhood-_a_Multifaceted_Collaborative_Filtering_Model.pdf)
+- Content-based CF / Context-aware CF
+  - there are so many ...
+- Advanced Matrix Factorization
+  - [Probabilistic Matrix Factorization](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf)
+  - [Fast Matrix Factorization for Online Recommendation with Implicit Feedback](https://dl.acm.org/citation.cfm?id=2911489)
+  - [Collaborative Filtering for Implicit Feedback Datasets](http://ieeexplore.ieee.org/document/4781121/)
+  - [Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence](https://dl.acm.org/citation.cfm?id=2959182)
+- Factorization Machine
+  - [Factorization Machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)
+  - [Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/citation.cfm?id=2959134)
+- Sparse LInear Method (SLIM)
+  - [SLIM: Sparse Linear Methods for Top-N Recommender Systems](http://glaros.dtc.umn.edu/gkhome/node/774)
+  - [Global and Local SLIM](http://glaros.dtc.umn.edu/gkhome/node/1192)
+- Learning to Rank
+  - [Wikipedia](https://en.wikipedia.org/wiki/Learning_to_rank)
+  - [BPR: Bayesian personalized ranking from implicit feedback](https://dl.acm.org/citation.cfm?id=1795167)
+  - [WSABIE: Scaling Up To Large Vocabulary Image Annotation](http://www.thespermwhale.com/jaseweston/papers/wsabie-ijcai.pdf)
+  - [Top-1 Feedback](http://proceedings.mlr.press/v38/chaudhuri15.pdf)
+  - [k-order statistic loss](http://www.ee.columbia.edu/~ronw/pubs/recsys2013-kaos.pdf)
+  - [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://dl.acm.org/citation.cfm?id=3015834)
+  - [The LambdaLoss Framework for Ranking Metric Optimization](https://dl.acm.org/citation.cfm?id=3271784)
+- Cold-start
+  - [Deep content-based music recommendation](https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation)
+  - [DropoutNet: Addressing Cold Start in Recommender Systems](https://papers.nips.cc/paper/7081-dropoutnet-addressing-cold-start-in-recommender-systems)
+- Network Embedding
+  - [awesome-network-embedding](https://github.com/chihming/awesome-network-embedding)
+  - [Item2vec](https://arxiv.org/abs/1603.04259)
+  - [entity2rec](https://dl.acm.org/citation.cfm?id=3109889)
+- Sequential-based
+  - [Factorizing Personalized Markov Chains for Next-Basket Recommendation](https://dl.acm.org/citation.cfm?id=1772773)
+  - [Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939)
+- Translation Embedding
+  - [Translation-based Recommendation](https://dl.acm.org/citation.cfm?id=3109882)
+  - [Translation-based Factorization Machines for Sequential Recommendation](https://dl.acm.org/citation.cfm?id=3240356)
+- Graph-Convolution-based
+  - [GraphSAGE: Inductive Representation Learning on Large Graphs](https://dl.acm.org/doi/10.5555/3294771.3294869)
+  - [PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://arxiv.org/abs/1806.01973)
+- Knowledge-Graph-based
+  - [Collaborative knowledge base embedding for recommender systems](https://dl.acm.org/doi/10.1145/2939672.2939673)
+  - [Knowledge Graph Convolutional Networks for Recommender Systems](https://dl.acm.org/citation.cfm?id=3313417)
+  - [KGAT: Knowledge Graph Attention Network for Recommendation](https://dl.acm.org/authorize.cfm?key=N688414)
+  - [Ripplenet: Propagating user preferences on the knowledge graph for recommender systems](https://dl.acm.org/doi/10.1145/3269206.3271739)
+- Deep Learning
+  - [Deep Neural Networks for YouTube Recommendations](https://ai.google/research/pubs/pub45530)
+  - [Deep Learning based Recommender System: A Survey and New Perspectives](https://arxiv.org/abs/1707.07435)
+  - [Neural Collaborative Filtering](https://dl.acm.org/citation.cfm?id=3052569)
+  - [Collaborative Deep Learning for Recommender Systems](http://www.wanghao.in/CDL.htm)
+  - [Collaborative Denoising Auto-Encoders for Top-N Recommender Systems](https://dl.acm.org/citation.cfm?id=2835837)
+  - [Collaborative recurrent autoencoder: recommend while learning to fill in the blanks](https://dl.acm.org/citation.cfm?id=3157143)
+  - [TensorFlow Wide & Deep Learning](https://www.tensorflow.org/tutorials/wide_and_deep)
+  - [Deep Neural Networks for YouTube Recommendations](https://research.google.com/pubs/pub45530.html)
+  - [Collaborative Memory Network for Recommendation Systems](https://arxiv.org/abs/1804.10862)
+  - [Variational Autoencoders for Collaborative Filtering](https://dl.acm.org/citation.cfm?id=3186150)
+  
+## Online Courses
+- [Recommender Systems Specialization](https://zh-tw.coursera.org/specializations/recommender-systems), University of Minnesota
+- [Introduction to Recommender Systems: Non-Personalized and Content-Based](https://zh-tw.coursera.org/learn/recommender-systems-introduction), University of Minnesota
+
+## RecSys-related Competitions
+- [Kaggle](https://www.kaggle.com/) - product recommendations, hotel recommendations, job recommendations, etc.
+- ACM RecSys Challenge
+- [WSDM Cup 2018](https://wsdm-cup-2018.kkbox.events/)
+- [Million Song Dataset Challenge](https://www.kaggle.com/c/msdchallenge)
+- [Netflix Prize](https://www.netflixprize.com/)
+
+## Tutorials
+- RecSys tutorials
+  - [2014](https://recsys.acm.org/recsys14/tutorials/)
+  - [2015](https://recsys.acm.org/recsys15/tutorials/)
+  - [2016](https://recsys.acm.org/recsys16/tutorials/)
+  - [2017](https://recsys.acm.org/recsys17/tutorials/)
+  - [2018](https://recsys.acm.org/recsys18/tutorials/)
+- [Kdd 2014 Tutorial - the recommender problem revisited](https://www.slideshare.net/xamat/kdd-2014-tutorial-the-recommender-problem-revisited)
+
+## Articles
+- [Matrix Factorization: A Simple Tutorial and Implementation in Python](http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/)
+
+## Conferences
+- [RecSys – ACM Recommender Systems](https://recsys.acm.org/)