Bayesian Personalized Sorting Based on Time Factors and Hot Recommendations

The chapter introduces a Bayesian Personalized Ranking model, BPR-TH, designed to address information overload in digital libraries. By incorporating time factors and hot recommendations, BPR-TH effectively handles massive distributed data and cold start problems, outperforming traditional BPR and File-path algorithms. The model is realized through user behavior feature extraction, model construction, and optimization, resulting in improved personalized recommendations both online and offline. Experimental results demonstrate BPR-TH's superior performance in accuracy, coverage, and recall, making it a promising solution for personalized digital library recommendations.