Covers: theory of Clustering and Dimensionality Reduction

- What is the problems of Sparsity as well as computational complexity?
- what are SVD, PCA and k-means?
- How to do MLE for estimating missing values in the Ratings Matrix?

K-means - https://stanford.edu/~cpiech/cs221/handouts/kmeans.html Maximum Likelihood Estimation - https://towardsdatascience.com/probability-concepts-explained-maximum-likelihood-estimation-c7b4342fdbb1 Section 2.5 - Explains how these are used for RecSys

Stanford, Towards Data Science

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Contributors

- Objectives
- Helps to quickly understand basic ideas of neighbourhood based collaborative filtering
- Potential Use Cases
- Build recommendation engines, Identify trends etc
- Who is This For ?
- BEGINNERPython developers

Click on each of the following **annotated items** to see details.

Resources4/4

ARTICLE 1. Ratings Matrix

- What is the basic data structure of this Matrix and what are its properties?
- What is a long-tail and how does it impact recommendation systems and what can we do about them?

30 minutes

ARTICLE 2. User-based and Item-based similarity Computations

- What are user-based and item-based models and how does one perform compared to another?
- How can we build a Rec Sys based on similarity based concepts like Cosine Similarity, Pearson Correlation etc? What does amplifying a similarity function do?
- What is mean centering and why do we use them? What are the common alternatives?
- How does inverse user frequency help to handle long-tail? Why should we handle long-tails in the first place?
- What is the computational complexity of the overall system? How to understand off-line phase and online-phase?

30 minutes

ARTICLE 3. Clustering and Similarity Based methods

- What is the problems of Sparsity as well as computational complexity?
- what are SVD, PCA and k-means?
- How to do MLE for estimating missing values in the Ratings Matrix?

20 minutes

ARTICLE 4. Regression View of Neighbourhood based method

- How the similarity coefficients that we use is similar to learned weights in a linear regression model?
- How to use Least Squares optimisation to learn the coefficients?
- How to handle sparsity and bias issues?
- How to understand regularization?

30 minutes

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