I spoke in Prepare AI conference (http://prepare.ai/conference/conference-agenda-details/) in May 2018. Though I spoke on a similar talk previously, this presentation is targeted to beginners in Recommendation Systems. In this presentation I talk a little more fundamental details.

1. How
Recommender Systems
work
by SK Reddy
Chief Product Officer AI
linkedin.com/in/sk-reddy/

2. https://medium.com/@humansforai/recommendation-engines-e431b6b6b446

3. where R - observed ratings, X - user embeddings, Y - item embeddings, λ is regularization parameter
Explicit feedback
including general bias and biases for user and item
Implicit Feedback
X, Y are user and item embeddings
http://dsnotes.com/post/2017-05-28-matrix-factorization-for-recommender-systems/
Basics
m users, n items, and an
extremely sparse rating matrix R
Additional side information matrix of user and
item are denoted by
The objective function of matrix factorization can be
written as

4. Types of machine learning algorithms used in recommender systems
https://arxiv.org/pdf/1511.05263.pdf

5. Google Play Recommendation System
https://arxiv.org/pdf/1606.07792.pdf
Apps recommendation pipeline overview
Wide & Deep model
Over 500 billion examples
Model’s prediction using logistic regression
where Y is the binary class label, σ(·) is the sigmoid function, φ(x) are the cross
product transformations of the original features x, and b is the bias term.
A. is the vector of all wide model weights, and are the weights
applied on the final activations a (lf ) .
Memorization: learning the frequent co-occurrence of items and exploiting the correlation.
Generalization: transitivity of correlation and explores new feature combinations that have
never or rarely occurred in the past.

6. http://www.kdd.org/kdd2017/papers/view/embedding-based-news-recommendation-for-millions-of-users
Yahoo News Recommender
Yahoo! JAPAN’s homepage on smartphones
Encoder for triplets of articlesBrowsing history and session
LSTM-based model
• Start with distributed representations of articles based on a
variant of the denoising autoencoder
• Generate user representations by using an RNN with
browsing histories as input sequences
• Match and list articles for each user based on the inner
product of article-user for relevance and article-article for
de-duplication

7. YouTube
Recommendation Engine
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
Ranking
Candidate Generation

8. Additional Stacked Denoising
Autoencoder(aSDAE)
Additional Denoising
Autoencoder(aDAE)
Ref: A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems.
The structure of the proposed hybrid model. The model contains three
components: the upper component and the lower component are two
aSDAEs which extract latent factor vectors for users and items respectively;
the middle component decomposes the rating matrix R into two latent
factor matrices
Hybrid model based on Autoencoders
Loss function of Autoencoder
Loss function of Denoising Autoencoder
(ADE)
Loss function of additional Denoising
Autoencoder (aDAE)

9. Thank you
SK Reddy
www.linkedin.com/in/sk-reddy/