Application of Machine Learning Algorithms in User Behavior Analysis and a Personalized Recommendation System in the Media Industry

Q3 Decision Sciences
Jialing Wang;Jun Zheng
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引用次数: 0

Abstract

Aimed at the multidimensional and nonlinear 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 realtime 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 coldstart 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.
机器学习算法在用户行为分析和媒体行业个性化推荐系统中的应用
针对媒体行业用户行为的多维和非线性特征,提出了一种基于混合机器学习的智能用户建模与推荐框架(MUMA)。该系统通过融合多源异构数据(点击流、观看时长、社交图谱、眼动热点),构建了一个时空双驱动的用户表征系统。核心技术突破包括:(1)设计动态兴趣感知网络(DIN),采用带时间衰减因子的混合LSTM-Transformer架构捕捉短期/长期行为模式;(2)开发基于异构信息网络(HIN)的跨域迁移学习模块,实现新闻/视频/广告业务协同推荐;(3)创新地将强化学习与因果推理相结合,构建土匪倾向混合推荐策略,平衡了探索与发展的矛盾。在系统实现层面,构建Flink+Redis实时特征工程管道,支持千维特征毫秒级更新;部署XGBoost-LightGBM双引擎排序模型,实现可解释的SHAP值推荐。实验表明,在头部视频平台的8亿条行为日志测试中,与传统协同过滤方法相比,该方案的点击率提高了29.7%,观看完成度提高了18.3%,冷启动用户推荐满意度提高了82.5% (A/B测试$P <;0.005美元)。本研究为媒体行业的用户行为建模提供了新的思路,也为个性化推荐系统的设计和实现提供了理论和实践参考。
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来源期刊
Journal of ICT Standardization
Journal of ICT Standardization Computer Science-Information Systems
CiteScore
2.20
自引率
0.00%
发文量
18
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