Owaiskhan9654/Sony-R.I.S.E-India-Hackathon-3rd-Place-Solution

Recent Sony RISE Research Team India organized and this is my Solution in which I secured 3rd Position. Recommender systems are among the most popular applications of data science today. They are used to predict the "rating" or "preference" that a user would give to an item. In this Challenge I have computed and extracted several Features in order to Build this Hybrid Collaborative Recommender System

/ 100

Experimental

This project helps businesses predict what products, movies, or content individual customers are most likely to enjoy, even for new users or less popular items. By analyzing past customer interactions and item characteristics, it suggests personalized recommendations. It takes in user interaction data and item details and outputs tailored suggestions for each customer. This is for product managers, content curators, and marketing teams looking to enhance user experience and drive engagement.

Use this if you need to offer highly personalized product or content recommendations to your customers, effectively handling both popular and niche items as well as new users.

Not ideal if your primary goal is simple popularity-based recommendations or if you have very little historical user interaction data to leverage.

e-commerce content-personalization customer-engagement marketing-strategy product-discovery

No License No Package No Dependents

Maintenance 10 / 25

Adoption 4 / 25

Maturity 8 / 25

Community 0 / 25

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