Application of Machine Learning Algorithms in User Behavior Analysis and a Personalized Recommendation System in the Media Industry
DOI:
https://doi.org/10.13052/jicts2245-800X.1313Keywords:
Media industry, personalized recommendation, dynamic interest-aware network, cross-domain transfer learning, reinforcement learning, causal inferenceAbstract
Aimed at the multi-dimensional and non-linear characteristics of user behavior in the media industry, this paper proposes an intelligent user modeling and recommendation framework (MUMA) based on hybrid machine learning. The system constructs a spatial-temporal dual-driven user characterization system by fusing heterogeneous data from multiple sources (clickstream, viewing duration, social graph, and eye-movement hotspot). The core technological breakthroughs include: (1) designing a dynamic interest-aware network (DIN) and adopting a hybrid LSTM–Transformer architecture with a time decay factor to capture short-term/long-term behavioral patterns; (2) developing a cross-domain migratory learning module based on a heterogeneous information network (HIN) to realize collaborative recommendation of news/video/advertising business; (3) innovatively combining reinforcement learning and causal inference to construct a bandit–propensity hybrid recommendation strategy, balancing the contradiction between exploration and development. At the system realization level, build a Flink+Redis real-time feature engineering pipeline to support millisecond update of thousands of dimensional features; deploy an XGBoost-LightGBM dual-engine ranking model to realize an interpretable recommendation by SHAP value. Experiments show that in the 800 million behavioral logs test of the head video platform, compared with traditional collaborative filtering methods, this scheme improves CTR by 29.7%, viewing completion by 18.3%, and cold-start user recommendation satisfaction by 82.5% (A/B test P<0.005). This study provides new ideas for user behavior modeling in the media industry, as well as theoretical and practical references for the design and implementation of personalized recommendation systems.
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