Abstract: 

In this training session, we will delve into the intricacies of building robust and scalable recommendation engines specifically tailored for online food delivery services. We will introduce a newly released dataset called the Delivery Hero Recommendation Dataset (DHRH) and understand how this can be used for training different recommendation models.We will explore the challenges faced in this domain and discuss the techniques and best practices to overcome them, ensuring our recommendation systems can handle large-scale operations and adapt to changing customer preferences.

Key Takeaways:
1. Understanding the Importance of Recommendation Engines in Online Food Delivery:
+ Explore the significance of recommendation engines in enhancing customer experience and driving business growth in the online food delivery industry.
+ Recognize the unique challenges and opportunities in providing personalized recommendations for diverse customer preferences and dietary restrictions.
2. Techniques for Building Robust Recommendation Engines:
+ Learn about this newly released dataset for online food delivery called Delivery Hero Recommendation Dataset (DHRD)
+ Learn about data collection, preprocessing, and feature engineering techniques to effectively leverage user preferences, item characteristics, and contextual information.
+ Discover the power of collaborative filtering, content-based filtering, and hybrid approaches to generate accurate and diverse recommendations.
3. Strategies for Scalability in Recommendation Systems:
+ Explore methods for handling large-scale datasets
+ Discuss scalable algorithms and model architectures such as matrix factorization, and ensemble methods to handle the increasing volume and velocity of data.
4. Addressing Cold Start and Real-Time Recommendations:
+ Explore solutions to tackle the cold start problem when dealing with new users and items with limited historical data.
+ Learn about real-time recommendation techniques, to provide timely and relevant suggestions.
5. Evaluating and Optimizing Recommendation Systems:
+ Understand common evaluation metrics such as precision, recall, and mean average precision to assess the performance of recommendation engines.
+ Discover methods for A/B testing, user studies, and feedback loops to continuously optimize and improve recommendation algorithms.

6. Addressing Diversity of Vendors:
+ Learn how we address diversity while keeping customer preferences and business metrics in balance.


By the end of this training session, participants will have gained insights into the techniques and strategies required to build robust and scalable recommendation engines specifically tailored for the online food delivery industry. They will be equipped with practical knowledge to overcome challenges, enhance customer experience, and drive business growth through personalized recommendations.

Bio: 

Raghav is a seasoned Data Science professional with over a decade’s experience of research & development of large-scale solutions in Finance, Digital Experience, IT Infrastructure and Healthcare for giants such as Intel, American Express, United HealthGroup and DeliverHero. He is an innovator with 10+ patents, a published author of multiple well received books & peer-reviewed papers and a regular speaker in leading conferences on topics in the areas of Generative AI, Recommendation Systems, Computer Vision, NLP, Deep Learning, Machine Learning and Augmented Reality.