Concept to Code: Learning Distributed Representation of Heterogeneous sources for Recommendation (Deep Learning Beginner/Intermediate)

by Omprakash Sonie (Flipkart, India), Surender Kumar (Flipkart, India) and Sudeshna Sarkar (IIT Kharagpur, India)

Deep Learning attempts to learn multiple levels of representations and abstractions from data. Some of the major Deep Learning techniques used in recommender systems are: Embedding methods for embedding different products based on content and transactions, feedforward multi-layer networks and auto-encoders for collaborative filtering, Convolutional Neural Network (CNN) for extracting features from content such as images, sound and text; and session-based recommendation with Recurrent Neural Networks (RNN).

The aim of this tutorial is to provide a conceptual understanding of learning distributed representation techniques by using various data sources including items, users, product images, review texts and ratings for a recommender system. They will be covered with sufficient mathematical background along with actual code.

A case study covering embeddings, RNN, and CNN will be presented along with the code walkthrough and what is going on behind the scenes.

The tutorial is for beginner and intermediate participants who have run a few projects in Python/Jupyter notebooks and would like to understand the high level math behind the techniques.


Tuesday, Oct 2, 2018, 09:00-10:30


Parq B/C

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