Data Science Meetup 5.0: Recap and Highlights!
Vaishali Raj Singh

Data Science Meetup 5.0: Recap and Highlights!

 Introduction 

We are thrilled to have you join us for another great session on data science. We are going deeper into machine learning, recommendation systems and the latest tech in the industry as we get together for our 5th event. 

We covered the latest in recommendation systems from the basics of collaborative filtering to neural networks and hybrid approaches. 

Something for everyone, regardless of level, learning curve or interest in how data science can impact company strategies. Our goal was to create a space where information is shared, ideas are thrown around and business relationships are formed. 

The first speaker of the day was Mr. Ashish Kasama, CTO at Lucent Innovation. His discussion was truly engaging. It was good and was very well received by the audience, also they got some very valuable information from it. The intro was humorous, and everyone loved it. 

Ashish Kasama, CTO of Lucent Innovation

QR Code for LinkedIn Profile:  

  • Mr. Ashish Kasama started with an explanation of QR codes that link directly to LinkedIn profiles. These are called “LinkedIn QR Codes” which encode a URL to a LinkedIn profile or page. Scanning this QR code with a reader will allow users to access the profile. 

Chocolate Game to understand Recommendation System:  

He introduced a chocolate game to explain recommendation systems: 

  • Setup: 6 chocolates divided into 2 slots, each slot having 3 chocolates. 
  • Slot 1: Chocolate A, Chocolate B, Chocolate C. 
  • Slot 2: Chocolate D, Chocolate E, Chocolate F. 
How It Works: 
  • Preference Identification: Chocolates are chosen based on similarities or user preferences. 
  • Slot Assignment: Items are grouped based on attributes, such as sweetness. 

Personalized Recommendations 

The system suggests items like those in the slots, to enhance user experience. 

Collaborative Filtering: 

  • Concept: Uses similarities between users to make recommendations. 
  • Applications: Common in online shopping and streaming services where recommendations are based on user preferences and behavior. 

Matrix Factorization with Recommendation Systems: 

A technique to factorize user-item interaction matrices to predict user preferences and improve recommendations. 

Neural Collaborative Filtering: 
  • Description: Uses neural networks to model complex user-item interactions and make more accurate recommendations. 

Challenges and Solutions in Hybrid Filtering: 

  • Challenges: Combining different recommendation techniques can be complex due to data sparsity and conflicting recommendations. 
  • Solutions: Use advanced algorithms and optimization techniques to integrate multiple filtering methods. 

Why Recommendation Systems are Better with GPUs? 

  • Benefits: GPUs accelerate the computations involved in recommendation algorithms like matrix factorization and neural networks resulting in faster processing and better performance. 

Our next speaker for the meetup was Mr. Krunal Prajapati, Project Director at Lucent Innovation. Everyone actively participated in this very interesting conversation. It was wonderful to see everyone understand and appreciate the concept. 

Krunal Prajapati, Project Director of Lucent Innovation

Introduction to AI based recommendation Systems 

 A recommendation system suggests options, based on user preferences or requirements.  

There are two main types:  

  • Personalized Recommendation Systems: Tailor suggestions based on individual user behavior (e.g., Netflix, Amazon).  
  • Non-Personalized Recommendation Systems: Provide the same recommendations to all users (e.g., Bestseller Lists, Trending Now). 

 Evolution of Recommendation Systems Includes:

  • Collaborative Filtering: Based on user-user or item-item similarity.  
  • Content-Based Filtering: Recommends items based on their attributes.  
  • Model-Based Methods: Uses algorithms to predict preferences.  

Applications in E-Commerce Product recommendation systems enhance the shopping experience by analyzing:  

  • User Data: Browsing and purchase history, search queries. 
  •  Product Data: Attributes, ratings, popularity. Behavioral Data: Clicks, cart additions, Wishlist's. 

Benefits of Recommender Systems Better Inventory Management Increased Conversions:

Through personalized product suggestions. Content-Based Filtering Uses machine learning to recommend items like those a user liked.  

Key algorithms include TF-IDF: Identifies important words. Cosine Similarity: Measures item similarity. K-Nearest Neighbors (KNN): Finds similar items. Neural Networks: Handles complex data for advanced recommendations.  

Advantages:

  • New items can be recommended if their features are known. Transparent and independent recommendations.  

Disadvantages:

  • Limited to items similar to past interactions.  

Collaborative Filtering User-Based: Recommends items liked by similar users. 

Item-Based: Recommends items like those previously liked.  

Hybrid Recommendation Systems : Combines various techniques (e.g., collaborative and content-based filtering) to optimize recommendations.  

Demo: AI Chatbot for Product Recommendations  

Conclusion 

We explored the latest recommendation systems, from collaborative filtering and matrix factorization to advanced neural networks and hybrid approaches and AI based recommendation systems. Stay tuned for future meetups as we continue to dive into data science innovations! 

Meetup Attendees

Also read, Insights from Lucent Innovation's Data Science Meetup: Advances in Computer Vision