water8394/flink-recommandSystem-demo
Flink RecommandSystem Demo
This project is a real-time product recommendation engine built on Apache Flink. It functions as a streaming behavioral analytics pipeline that processes raw logs to derive user interests and product popularity trends.
The system utilizes a collaborative filtering engine to compute item similarity via cosine similarity and shared user interaction patterns. It employs a hybrid re-ranking pipeline that combines global popularity lists with personalized user profiles to sort recommended items.
The architecture incorporates a wide-column user store using HBase for persistent behavioral records and a Redis-backed cache for real-time item heat lists. The pipeline includes capabilities for behavioral interest extraction, interaction interval analysis, and a performance dashboard for monitoring log ingestion rates.
Features
- Recommendation Engines - Implements a real-time recommendation engine for delivering personalized product suggestions based on user behavior and item similarity.
- Real-Time Recommendation Systems - Building a real-time system that analyzes behavior and popularity to deliver personalized product suggestions.
- Retrieval Re-ranking - Implements a re-ranking pipeline that combines global trends with personalized profiles to refine recommended item lists.
- User Profile Extraction - Extracts user interest tags and behavioral preferences from raw interaction logs for profile creation.
- Flink-Based Recommenders - A real-time product recommendation engine built on Apache Flink for processing behavioral logs.
- Real-Time Analytics - Processes streaming data with low latency to provide immediate insights into user interests and item trends.
- Stream Analytics Processing - Uses Apache Flink to calculate rolling aggregates and popularity metrics over time-based windows.
- Similarity Search - Identifies related products by calculating mathematical proximity via cosine similarity and collaborative filtering.
- User Behavior Analysis - Analyzes individual user activity history and interaction logs to derive detailed interest personas.
- Behavioral Analytics Pipelines - Provides a streaming behavioral analytics pipeline that routes event logs through Flink and NoSQL stores.
- Item-to-Item Similarity - Calculates mathematical similarity between product attribute vectors to identify and suggest related items.
- Collaborative Filtering Systems - Implements a collaborative filtering engine that predicts preferences via user-item interaction matrices.
- Interest Modeling - Analyzes behavioral interaction logs to extract product attributes and build user interest profiles for real-time recommendations.
- Real-Time Popularity Tracking - Tracks real-time item popularity using time windows and caching for rapid retrieval.
- Behavioral Timing Analysis - Determines user interest levels by calculating the time intervals between distinct product interaction events.
- Recommendation Heat-Lists - Provides a Redis-backed cache for storing and retrieving real-time product heat lists to minimize latency.
- Redis Caching Layers - Employs Redis caching layers to store frequently accessed real-time popularity metrics.
- Trending Item Tracking - Tracks real-time item trends using time windows and efficient frequency estimation.
- Wide-Column Stores - Utilizes a distributed wide-column store via HBase to maintain scalable user portraits and behavioral records.
- HBase Profile Stores - Uses HBase as a wide-column store to maintain persistent user portrait tags and behavioral records.
