显式反馈与隐式反馈相结合，可以有效提升推荐性能.但是现有的融合显式反馈与隐式反馈的推荐系统存在未能发挥隐式反馈数据缺失值反映用户隐藏偏好的能力，或者未能保留显式反馈数据反映用户偏好程度的能力的局限性.为了解决这个问题，提出了一种融合显式反馈与隐式反馈的协同过滤推荐算法.该算法分为两个阶段：第1阶段利用加权低秩近似处理隐式反馈数据，训练出隐式用户/物品向量；第2阶段引入了基线评估，同时将隐式用户/物品向量作为补充，通过显隐式用户/物品向量结合，训练得出用户对物品的预测偏好程度.该算法与多个典型算法在标准数据集上进行了实验比较，其可行性和有效性得到验证.

The combination of explicit and implicit feedback can effectively improve recommendation performance. However, the existing recommendation systems have some disadvantages in integrating explicit feedback and implicit feedback, i.e., the ability of implicit feedback to reflect hidden preferences from missing values is ignored or the ability of explicit feedback to reflect users' preferences is not fully utilized. To address this issue, this study proposes an explicit and implicit feedback based collaborative filtering algorithm. The algorithm is divided into two stages, where the first stage deals with implicit feedback data by weighted low rank approximation to train implicit user/item vectors, and the second stage introduces a baseline estimate and uses the implicit user/item vectors as supplementaries to the explicit user/item vectors. Through the combination of explicit and implicit user/item vectors, the predictions of users' preferences for items can be obtained by training. The proposed algorithm is compared with several typical algorithms on standard datasets, and the results confirm its feasibility and effectiveness.

国家自然科学基金（61876193，61822601，61773050，61632004）；广东省自然科学基金-杰出青年基金（2016A030306014）；广东特支计划"科技创新青年拔尖人才"（2016TQ03X542）；北京市自然科学基金（Z180006）；国家重点研发计划（2017YFC1703506）；中央高校基本科研业务费专项资金（2019JBZ110）