基于时序背景 LDA 与协同过滤的混合推荐模型

摘要

The existing personalized recommendations often ignore the effect on the users’ behavior caused by the time informa-tion, resulting in a lower forecast accuracy.According to the user attribute information and user rating information, this paper sets up a mixture recommendation model ( TLDA-CF) based on the temporal context-aware LDA model and collaborative filtering mod-el.It improves the recommendation efficiency through offline and online recommendation;respectively establishes the LDA mod-els based on the user rating information and the project’ s access amount in every period of time to solve the problem of data spar-sity;sets a dynamic weight to balance the user selection trend to improve the accuracy;and uses user clustering and collaborative filtering algorithm to predict the users’ preferences for those new users with no rating information, so as to solve the cold start problem.The results of the proposed algorithm’ s experiment on MovieLens datasets indicate that this algorithm is superior to the traditional LDA model on the recommendation recall rate, accuracy, and F1 values.%现有的个性化推荐通常会忽略时间信息对用户行为的影响，导致预测准确性较低。本文根据用户属性信息和用户评分信息，建立基于时序背景LDA与协同过滤的混合模型（ TLDA-CF）。通过离线与在线推荐提高推荐效率；根据用户对项目的评分信息，以及各时间段内项目的被访问量分别建立LDA模型，解决数据稀疏性问题；设置动态权值平衡用户选择趋势提高推荐准确性；对于没有评分信息的新用户，采用用户聚类，引用协同过滤算法预测新用户喜好，解决冷启动问题。将该算法应用在MovieLens数据集上，实验结果表示，该算法在推荐召回率和准确率、F1值上都优于传统的LDA模型。