Project 1: Instagram Reels Recommendation System
We’re going to build a base framework for recommendation system for Instagram. It will suggest videos to users based on their watch history, likes, and follows. Imagine it like giving each user their own personalized TV channel!
Radix Tree Data Structure: To Store and Quickly Search User Interests
What it is: A Radix Tree, also known as a Patricia Trie, is a compacted version of a Trie that stores keys with long common prefixes in compressed nodes.
Why Radix Tree: Radix Trees are used for efficient storage and retrieval of keys with long common prefixes, making them suitable for scenarios where memory efficiency is critical.
Pros and Cons:
- Pros: Memory-efficient due to node compression; optimal for keys with long common prefixes.
- Cons: More complex to implement compared to standard Tries; less efficient for datasets with short and varied key lengths.
GitHub Link: Example Radix Tree Implementation
References: IEEE Transactions on Knowledge and Data Engineering
Heap/Priority Queue: To Rank and Retrieve Top Recommendations
What it is: A priority queue is a data structure where each element has a priority, and elements are served based on their priority.
Why Instagram Uses It: Heaps are used to rank recommendations based on relevance, ensuring that the most relevant videos are suggested to users first.
Pros and Cons:
- Pros: Efficient for dynamically selecting top elements; simple to implement.
- Cons: Not ideal for unordered data; less efficient for non-top elements.
GitHub Link: Example Priority Queue Implementation
References: IEEE Transactions on Software Engineering