基于显式与隐式反馈信息的概率矩阵分解推荐

针对现有的基于用户显式反馈信息的推荐系统推荐准确率不高的问题,提出了一种基于显式与隐式反馈信息的概率矩阵分解推荐方法。该方法综合考虑了显示反馈信息和隐式反馈信息,在对用户信任关系矩阵和商品评分矩阵进行概率分解的同时加入了用户评分记录的隐式反馈信息,优化训练模型参数,为用户提供精确的预测评分。实验结果表明,该方法可以有效地获得用户偏好,产生大量的准确度高的推荐。

Probabilistic matrix factorization recommendation with explicit and implicit feedback

Focusing on the issue that the recommender systems with explicit feedback drastically degrade the accuracy, the recommender technique using probabilistic matrix factorization with explicit and implicit feedback was proposed. So the explicit and implicit feedback was taken into account in this method. Firstly, user trust relationship matrix and user-item matrix were factorized using probabilistic matrix factorization to mix the feedback of user rating records. Then the model was trained to provide users with accurate prediction. The experimental results show that this technique can obtain user preferences effectively and produce large amounts of highly accurate recommendations.