faizanahemad/Hybrid-Weighted-Embedding-Recommender
A Hybrid Recommendation system which uses Content embeddings and augments them with collaborative features. Weighted Combination of embeddings enables solving cold start with fast training and serving
This project helps e-commerce stores, streaming services, or content platforms provide personalized suggestions to their users. It takes in information about items (like movie genres or product descriptions) and user interactions (like past purchases or ratings) to suggest relevant new items. This is useful for anyone managing a platform where recommending items to users is key to engagement and sales.
No commits in the last 6 months.
Use this if you need a recommendation system that can effectively suggest items even to new users or for new products, by intelligently combining item details with user behavior.
Not ideal if your recommendation needs are simple and you don't have detailed content information for your items, or if you prefer a system that focuses purely on collaborative filtering without leveraging item attributes.
Stars
17
Forks
3
Language
Python
License
MIT
Category
Last pushed
Dec 08, 2022
Commits (30d)
0
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