Instagram processes massive amounts of user interaction data, content metadata, and social graph information. The following data structures are likely used to support the ranking of posts and videos, especially those you’ve liked or from accounts you follow:

1. Graph Data Structures (Social Graph):
   • Purpose: Represents the relationships between users (e.g., who follows whom, mutual interactions like likes, comments, or DMs).
   • Structure: Directed graph or adjacency list.
     • Nodes: Represent users, posts, or content (e.g., Reels, Stories).
     • Edges: Represent interactions (e.g., follows, likes, comments, shares, DMs). Edges may have weights based on interaction frequency or recency.
     • Usage: Instagram uses a social graph to determine “closeness” between you and followed accounts. For example, frequent likes or DMs with a specific account increase edge weights, prioritizing their content in your feed.
     • Implementation: Likely stored in a distributed graph database like Apache Cassandra or Meta’s TAO (The Associations and Objects), which is designed for real-time social graph queries.
   • Example: If you like posts from a followed account frequently, the edge weight between you and that account increases, making their posts appear higher in your feed.
2. Hash Tables (Dictionaries):
   • Purpose: Fast lookup of user profiles, posts, or metadata (e.g., hashtags, timestamps, engagement metrics).
   • Structure: Key-value pairs where keys might be user IDs, post IDs, or hashtags, and values store attributes like engagement counts (likes, comments), timestamps, or content features.
   • Usage: Used for quick retrieval of post information when ranking content or displaying likes. For example, when Instagram shows who liked a post, it uses a hash table to map post IDs to lists of liker IDs, prioritizing those from followed accounts.
   • Implementation: Likely implemented in memory using Redis or Memcached for caching frequently accessed data, with persistent storage in a database like MySQL or PostgreSQL.
3. Priority Queues or Heaps:
   • Purpose: Rank posts or Reels in real-time based on a scoring function (e.g., relevance score based on likes, recency, or engagement).
   • Structure: Max-heap or min-heap, where the priority is determined by a score combining factors like likes, comments, recency, and user interaction history.
   • Usage: When generating your feed, Instagram calculates a relevance score for each post from followed accounts and uses a priority queue to select the top-ranked content. For example, a post with many recent likes from accounts you interact with gets a higher score.
   • Implementation: Likely in-memory for real-time ranking, with precomputed scores stored in a distributed system like Apache Kafka for event streaming.
4. Inverted Indexes:
   • Purpose: Efficiently search and retrieve posts based on hashtags, captions, or other metadata.
   • Structure: Maps keywords (e.g., hashtags, locations) to lists of post IDs containing those keywords.
   • Usage: When you engage with posts containing specific hashtags or from certain locations, Instagram uses an inverted index to find similar content from followed accounts for the Explore page or feed.
   • Implementation: Likely powered by a search engine like Elasticsearch or a custom-built indexing system for fast content retrieval.
5. Sparse Matrices or Vectors:
   • Purpose: Represent user-content interactions for recommendation models.
   • Structure: A matrix where rows represent users, columns represent posts or videos, and entries indicate interactions (e.g., likes = 1, no interaction = 0).
   • Usage: Used in collaborative filtering models to identify patterns in likes or views across users and recommend similar content from followed accounts. For example, if you like posts similar to those liked by users with similar behavior, those posts are prioritized.
   • Implementation: Stored in compressed sparse row (CSR) format for efficiency, likely processed using machine learning frameworks like TensorFlow or PyTorch.
6. Time-Series Data Structures:
   • Purpose: Track engagement metrics (e.g., likes, comments) over time to prioritize recent content.
   • Structure: Time-series databases or logs storing timestamps of interactions.
   • Usage: Instagram prioritizes recent posts or Reels from followed accounts, especially if they’ve received recent likes or comments. Time-series data helps calculate engagement velocity (e.g., likes per hour).
   • Implementation: Likely uses a time-series database like InfluxDB or Meta’s internal systems for logging and analyzing engagement trends.
7. Bloom Filters:
   • Purpose: Efficiently check if a user has interacted with a post (e.g., liked or viewed) without querying the full database.
   • Structure: Probabilistic data structure for membership testing.
   • Usage: When ranking posts, Instagram may use Bloom filters to quickly check if you’ve liked a post from a followed account, reducing database queries.
   • Implementation: In-memory for low-latency checks, integrated with caching systems.

Algorithms Used by Instagram

Instagram’s recommendation system combines machine learning, heuristic-based ranking, and real-time processing. The following algorithms are likely used to prioritize liked posts or videos from followed accounts:

1. Machine Learning Models (Neural Networks):
   • Type: Deep neural networks (DNNs), likely convolutional neural networks (CNNs) for visual content and recurrent neural networks (RNNs) or transformers for sequential data (e.g., user interaction history).
   • Purpose: Predict the likelihood of you engaging with a post or Reel based on features like likes, comments, content type, and your interaction history with the account.
   • How It Works:
     • Features extracted from posts (e.g., image embeddings, audio features in Reels, text in captions) and user behavior (e.g., likes, time spent viewing) are fed into a neural network.
     • The model outputs a relevance score for each post, determining its rank in your feed.
     • For example, if you frequently like Reels from a followed account, the model assigns a higher score to their new Reels.
   • Implementation: Likely uses Meta’s PyTorch-based infrastructure, with pre-trained models fine-tuned on user data. Models are updated periodically to adapt to new trends (e.g., trending audio in Reels).
   • Example: A DNN might predict you’ll like a Reel from a followed account if it uses a trending audio track you’ve engaged with before.
2. Collaborative Filtering:
   • Type: User-based or item-based collaborative filtering.
   • Purpose: Recommend posts or Reels based on similarities between users or content.
   • How It Works:
     • User-Based: If you and another user both like posts from a specific followed account, the algorithm recommends other posts that user liked from accounts you follow.
     • Item-Based: If you like a post, the algorithm recommends similar posts (e.g., same hashtags, similar visuals) from other followed accounts.
     • Uses cosine similarity or matrix factorization (e.g., Singular Value Decomposition) on the user-content interaction matrix.
   • Implementation: Likely uses sparse matrix operations with frameworks like Apache Spark or TensorFlow.
   • Example: If you like posts with #fitness from a followed account, the algorithm may prioritize similar posts from other followed accounts.
3. Content-Based Filtering:
   • Type: Feature-based recommendation.
   • Purpose: Recommend posts or Reels based on their content features (e.g., hashtags, captions, visual elements).
   • How It Works:
     • Extracts features from posts (e.g., image embeddings via CNNs, text embeddings via BERT or similar NLP models).
     • Compares these features to your interaction history (e.g., posts you’ve liked) to recommend similar content from followed accounts.
     • For example, if you like posts with #travel from a followed account, the algorithm prioritizes other travel-related posts from accounts you follow.
   • Implementation: Uses precomputed embeddings stored in a vector database (e.g., FAISS) for fast similarity searches.
4. Ranking Algorithm (Weighted Scoring):
   • Type: Custom heuristic-based ranking with machine learning.
   • Purpose: Sort posts in your feed or Stories based on a weighted combination of signals (e.g., likes, recency, interaction history).
   • How It Works:
     • Assigns a relevance score to each post using a formula like: text
     • CopyScore = w1 * Likes + w2 * Comments + w3 * Shares + w4 * Recency + w5 * Interaction_Strength where weights (w1, w2, etc.) are tuned by machine learning models.
     • Posts from followed accounts with recent likes or high engagement are ranked higher.
     • Uses a priority queue to select the top-k posts for your feed.
     • Implementation: Real-time ranking with precomputed scores stored in a distributed cache (e.g., Redis) for low-latency delivery.
5. Gradient Boosting (e.g., XGBoost, LightGBM):
   • Purpose: Fine-tune ranking models by combining multiple weak predictors.
   • How It Works:
     • Uses decision trees to model complex relationships between features (e.g., likes, user interactions, post metadata).
     • For example, it might learn that posts from followed accounts with many likes within the first hour are more relevant to you.
   • Implementation: Likely used in offline training of ranking models, with results deployed to production systems.
6. Reinforcement Learning:
   • Purpose: Optimize long-term user engagement by balancing exploration (showing new content) and exploitation (showing content you’re likely to like).
   • How It Works:
     • Models like Multi-Armed Bandits or Deep Q-Networks learn which posts from followed accounts maximize engagement (e.g., likes, time spent).
     • For example, if you consistently like Reels from a followed account, the algorithm reinforces showing their content while occasionally testing new content types.
   • Implementation: Likely used in Meta’s recommendation systems for dynamic adjustments to the feed.
7. Clustering Algorithms (e.g., K-Means, DBSCAN):
   • Purpose: Group similar users or content for recommendations.
   • How It Works:
     • Clusters users based on liking patterns or content based on features (e.g., hashtags, visual style).
     • For example, if you’re in a cluster of users who like fitness Reels from followed accounts, the algorithm prioritizes similar content.
   • Implementation: Used in offline processing to precompute user or content clusters, stored in a distributed system like Hadoop or Spark.
8. Time-Decay Algorithms:
   • Purpose: Prioritize recent content to keep the feed fresh.
   • How It Works:
     • Applies an exponential decay function to engagement metrics (e.g., likes, comments) based on post age.
     • For example, a post from a followed account with 100 likes posted an hour ago is prioritized over one with 200 likes posted a week ago.
   • Implementation: Integrated into the ranking formula, likely computed in real-time.