# water8394/flink-recommandsystem-demo **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/water8394-flink-recommandsystem-demo).** 4,473 stars · 1,482 forks · Java ## Links - GitHub: https://github.com/water8394/flink-recommandSystem-demo - awesome-repositories: https://awesome-repositories.com/repository/water8394-flink-recommandsystem-demo.md ## Topics `flink` `flink-examples` `flink-hbase` `flink-kafka` `flink-redis` `recommand` `recommander-system` ## Description 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. ## Tags ### Business & Productivity Software - [Recommendation Engines](https://awesome-repositories.com/f/business-productivity-software/recommendation-engines.md) — Implements a real-time recommendation engine for delivering personalized product suggestions based on user behavior and item similarity. ([source](https://github.com/water8394/flink-recommandsystem-demo#readme)) ### Artificial Intelligence & ML - [Real-Time Recommendation Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/real-time-recommendation-systems.md) — Building a real-time system that analyzes behavior and popularity to deliver personalized product suggestions. - [Retrieval Re-ranking](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-re-ranking.md) — Implements a re-ranking pipeline that combines global trends with personalized profiles to refine recommended item lists. - [Real-Time Popularity Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/real-time-popularity-tracking.md) — Tracks real-time item popularity using time windows and caching for rapid retrieval. ([source](https://github.com/water8394/flink-recommandsystem-demo#readme)) ### Part of an Awesome List - [User Profile Extraction](https://awesome-repositories.com/f/awesome-lists/ai/reasoning-frameworks/cognitive-reasoning-patterns/memory-pattern-extraction/user-profile-extraction.md) — Extracts user interest tags and behavioral preferences from raw interaction logs for profile creation. - [Flink-Based Recommenders](https://awesome-repositories.com/f/awesome-lists/ai/recommender-systems/flink-based-recommenders.md) — A real-time product recommendation engine built on Apache Flink for processing behavioral logs. ### Data & Databases - [Real-Time Analytics](https://awesome-repositories.com/f/data-databases/real-time-analytics.md) — Processes streaming data with low latency to provide immediate insights into user interests and item trends. - [Stream Analytics Processing](https://awesome-repositories.com/f/data-databases/real-time-analytics/stream-analytics-processing.md) — Uses Apache Flink to calculate rolling aggregates and popularity metrics over time-based windows. - [Similarity Search](https://awesome-repositories.com/f/data-databases/similarity-search.md) — Identifies related products by calculating mathematical proximity via cosine similarity and collaborative filtering. ([source](https://github.com/water8394/flink-recommandsystem-demo#readme)) - [User Behavior Analysis](https://awesome-repositories.com/f/data-databases/user-behavior-analysis.md) — Analyzes individual user activity history and interaction logs to derive detailed interest personas. - [Behavioral Timing Analysis](https://awesome-repositories.com/f/data-databases/date-and-time-libraries/interval-comparisons/behavioral-timing-analysis.md) — Determines user interest levels by calculating the time intervals between distinct product interaction events. ([source](https://github.com/water8394/flink-recommandsystem-demo#readme)) - [Recommendation Heat-Lists](https://awesome-repositories.com/f/data-databases/filesystem-cache-stores/cache-store-information-retrievers/recommendation-heat-lists.md) — Provides a Redis-backed cache for storing and retrieving real-time product heat lists to minimize latency. - [Redis Caching Layers](https://awesome-repositories.com/f/data-databases/redis-caching-layers.md) — Employs Redis caching layers to store frequently accessed real-time popularity metrics. - [Trending Item Tracking](https://awesome-repositories.com/f/data-databases/trending-item-tracking.md) — Tracks real-time item trends using time windows and efficient frequency estimation. - [Wide-Column Stores](https://awesome-repositories.com/f/data-databases/wide-column-stores.md) — Utilizes a distributed wide-column store via HBase to maintain scalable user portraits and behavioral records. ### Software Engineering & Architecture - [Behavioral Analytics Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/data-processing-pipelines/streaming-data-pipelines/behavioral-analytics-pipelines.md) — Provides a streaming behavioral analytics pipeline that routes event logs through Flink and NoSQL stores. ### User Interface & Experience - [Item-to-Item Similarity](https://awesome-repositories.com/f/user-interface-experience/form-builders/builder-item-collapsers/builder-item-managers/list-item-markers/item-to-item-similarity.md) — Calculates mathematical similarity between product attribute vectors to identify and suggest related items. - [Collaborative Filtering Systems](https://awesome-repositories.com/f/user-interface-experience/form-builders/builder-item-collapsers/builder-item-managers/list-item-markers/item-to-item-similarity/collaborative-filtering-systems.md) — Implements a collaborative filtering engine that predicts preferences via user-item interaction matrices. ### Web Development - [Interest Modeling](https://awesome-repositories.com/f/web-development/user-profiles/interest-modeling.md) — Analyzes behavioral interaction logs to extract product attributes and build user interest profiles for real-time recommendations. ([source](https://github.com/water8394/flink-recommandsystem-demo#readme)) ### Security & Cryptography - [HBase Profile Stores](https://awesome-repositories.com/f/security-cryptography/identity-access-management/identity-management/user-management/user-profile-management/hbase-profile-stores.md) — Uses HBase as a wide-column store to maintain persistent user portrait tags and behavioral records.