Recommendation System using Deep Learning
What you'll learn, and how you can apply it
- Get a thorough introduction to recommendation systems and paradigms across domains
- Gain an end-to-end view of deep learning-based recommendation and learning-to-rank systems
- Understand practical considerations and guidelines for building and deploying recommendation systems for their own problems
Who is this presentation for?
- You're a Data Scientist
- You're a Product Manager
- You're a Data Analyst
- You're a Software Engineer
- This is a hands-on course and hence, participants should be comfortable with programming. Familiarity with python data stack is preferred.
- Prior knowledge of machine learning principles is required. Participants should have some practice with basic machine learning problems e.g. regression, classification.
- While the deep learning concepts will be taught in an intuitive way, some prior knowledge of linear algebra and calculus would be helpful.
Hardware and/or installation requirements:
- All notebooks and required data-sets will be provided using a cloud hosted environment. No additional downloads required.
- Attendees will only require to have a browser with internet connectivity on their own laptop.
Sure. I do marathons…. on Netflix.
In the digital world, recommendation systems play a significant role – both for the users and for the company. For the users, a new world of options are thrown up – that were hitherto tough to find. For companies, it helps drive up user engagement and satisfaction, directly impacting their bottom line. If you’ve shopped on an e-commerce site or watched a movie on an on-demand video platform you would’ve seen options like: “People who viewed this product also viewed…” “Products similar to this one…”. These are the results from recommendation systems (recsys).
In this two-days workshop, you will learn the different paradigms of recommendation systems and get introduced to the usage of deep-learning based approaches . By the end of the workshop, you will have enough practical hands-on knowledge to build, select, deploy and maintain a recommendation system for your problem.
Session #1: Introduction
- Why build recommendation systems? Scope and evolution of recsys
- Challenges: accuracy, ranking, relevance, novelty, serendipity & diversity
- Paradigms in recommendations: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensemble
- Key concepts in recsys: Explicit vs. implicit feedback, User-Item matrix, domain signals: location, time, context, social
- Why use deep learning for recsys? Traditional vs deep learning approaches
- Primer on deep learning with examples and use-cases
Session #2: Content-Based
- Introduction to the case #1: product recommendation
- Environment setup for hands-on session
- Feature extraction using deep learning: Embeddings for Hetrogenous data
- Exercise: Recommending items using similarity measures_
Session #3: Colloborative-Filtering
- Overview of traditional Colloborative-Filtering for recsys
- Primer on deep learning approaches: Deep matrix factorisation & Auto-Encoders
- Exercise: Recommending items using Colloborative-Filtering_
Session #4: Learning-to-Rank
- Why learning-to-rank? Prediction vs Ranking
- Rank-learning approaches: pointwise, pairwise and listwise
- Deep learning approach to combine prediction and ranking
- Exercise: Recommending items using Learning-to-Rank_
Session #5: Hybrid Recommender
- Introduction to the case #2: text recommendation
- Combining content-based and collaborative filtering
- Primer on Wide & Deep Learning for Recommender Systems
- _Exercise: Recommending items using Hybrid recommender
Session #6: Time and Context
- Adding temporal component: window and decay-based
- Adding context context through group recommendations
- Dynamic and Sequential modelling using Recurrent Neural Networks
- Exercise: Recommending items using RNN recommender
Session #7: Deployment & Monitoring
- Deploying the recommendation system models
- Measuring improvements from recommendation system
- Improving the models based on the feedback from production
- Architecture design for recsys: Offline, Nearline and Online
Session #8: Evaluation, Challenges & Way Forward
- A/B testing for recommendation systems
- Challenges in recsys: Building explanations. Model debugging, Scaling-out & up, fairness. accountability and trust
- Bias in recsys: training data, UI → Algorithm → UI, private
- When not to use deep learning for recsys
- Recap and next steps, Learning Resources
About your instructors
Bargava Subramanian is a deep learning engineer and cofounder of a boutique AI firm Binaize Labs in Bangalore, India. He has 15 years’ experience delivering business analytics and machine learning solutions to B2B companies, and he mentors organizations in their data science journey. He holds a master’s degree from the University of Maryland at College Park. He’s an ardent NBA fan.
Amit Kapoor is interested in learning and teaching the craft of telling visual stories with data. At narrativeVIZ Consulting, Amit uses storytelling and data visualization as tools for improving communication, persuasion, and leadership through workshops and trainings conducted for corporations, nonprofits, colleges, and individuals. Amit also teaches storytelling with data for executive courses as a guest faculty member at IIM Bangalore and IIM Ahmedabad. Amit’s background is in strategy consulting, using data-driven stories to drive change across organizations and businesses. He has more than 12 years of management consulting experience with AT Kearney in India, Booz & Company in Europe, and startups in Bangalore. Amit holds a BTech in mechanical engineering from IIT, Delhi, and a PGDM (MBA) from IIM, Ahmedabad.
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