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.
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He introduced a chocolate game to explain recommendation systems:
Personalized Recommendations
The system suggests items like those in the slots, to enhance user experience.
Collaborative Filtering:
Matrix Factorization with Recommendation Systems:
A technique to factorize user-item interaction matrices to predict user preferences and improve recommendations.
Neural Collaborative Filtering:Challenges and Solutions in Hybrid Filtering:
Why Recommendation Systems are Better with GPUs?
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.
A recommendation system suggests options, based on user preferences or requirements.
There are two main types:
Evolution of Recommendation Systems Includes:
Applications in E-Commerce Product recommendation systems enhance the shopping experience by analyzing:
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:
Disadvantages:
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
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!
Also read, Insights from Lucent Innovation's Data Science Meetup: Advances in Computer Vision
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