Cross-Domain Transfer Learning Framework for Personalized Recommendation Systems Leveraging Heterogeneous Big Data Streams
Аннотация
Heterogeneous big data streams are used to engineer an effective cross-domain transfer learning framework for personalized recommendation systems that effectively leverages. Existing recommendation models have challenges with dealing with data sparsity, weak domain adaptability, and a lack of the capability to handle multi-format real-time data, thus not performing as effectively in dynamic environments. The suggested DANN-CF framework integrates Domain-Adversarial Neural Networks (DANN) and Neural Collaborative Filtering (NCF) to allow the model to learn domain-invariant user tastes from diverse data sources such as ratings, reviews, and item features. The Douban Dataset (Ratings, Reviews, Side Information) validates the system's performance across different domains such as movies, books, and music. Implemented on the Python platform with Apache Flink and TensorFlow on simulated data streams, DANN-CF improves precision while promoting scalability and flexibility. It greatly enhances recommendation precision over traditional single-domain models with an RMSE value of 0.1687, personalized content presentation, and smart education through accurate, real-time, cross-context recommendations.