Practical Recommendation Systems: Scalability, Accuracy, Latency

Nov 2019

18 Mon

19 Tue

20 Wed

21 Thu

22 Fri

23 Sat 08:30 AM – 05:30 PM IST

24 Sun

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Accepting submissions till 01 Nov 2019, 04:20 PM

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##About the 2019 edition:

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Myths and realities of labelling datasets for Deep Learning.
Practical experience with using Knowledge Graphs for different use cases.
Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
Pros and cons of using custom and open source tooling for AI/DL/ML.

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Practical Recommendation Systems: Scalability, Accuracy, Latency

Submitted Apr 30, 2019

Section: Tutorials Technical level: Intermediate Session type: Tutorial

This tutorial shall cover traditional and modern recommendation systems from a perspective of practical application, in an easy question-answer format. The content of this tutorial is derived from multiple state-of-the-art research papers as well as classical text books on recommendation systems.

Questions covered:

Can a good recommendation system change the entire landscape of a market?
- Offline vs online
- Search vs discovery
- Ultimate user experience
What are traditional recommendation systems and why they don’t work well in today’s world?
- Matrix factorization
- Content based recommendation
- Collaborative filtering based recommendation
- Why are the traditional recommendation systems not enough
What are modern recommendation systems and what is the cost?
- Hybrid recommendation systems
- Machine Learning based recommendation systems
- Era of Deep Learning, Factorization Machines etc
- Customised evaluation techniques and loss function to achieve end goals better
Conclusion: can we build a large scale awsome recommendation system like Youtube, Netflix or Spotify?
- What is these companies’ secret recipe behind their recommendation engine
- Latency, scalability, accuracy
- Infrastructure and model deployment cost

Outline

traditional recommendation systems
modern recomendation systems
Scalability, Accuracy, Latency
Cost benefit analysis
Examples of state-of-the-art recommendation systems in the market

Requirements

Nothing specific. People should have a basic understanding of Machine Learning.

Speaker bio

Kunal Kishore completed his Bachelor of technology degree from IIT Kharagpur in Electronics and Communication Engineering. Currently he works as Research Scientist at Inmobi where he leads the data science efforts on Inmobi’s CDP offering. He has previously worked on data science areas such as large scale content recommendation systems, ad response prediction for display advertising bidder and e-commerce product recommendation.

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