Bayesian Personalized Sorting Based on Time Factors and Hot Recommendations

Aiming at the problems of strict preference judgment and cold start in Bayesian personalized ranking(BPR), an improved ranking model is proposed, which considers the influence of time and incorporates hot recommendations. By extracting user behavior features, constructing an optimized BPR model, and processing recommendation results, we establish BPR-TH for realizing personalized online (or offline) recommendation of digital library information. By Comparing with other two similar algorithms, the experimental results show that this model performs better.