耦合用户公共特征的单类协同过滤推荐算法

将显式特征与隐式反馈相结合是提高单类协同过滤(OCCF)推荐准确性的常用方法.但目前的研究一般是直接将原始显式特征或交叉特征集成到OCCF模型中,因其难以判断哪些显式特征是真正重要的,故很难获得显著的性能改进.基于此,提出了一种耦合用户公共特征的单类协同过滤推荐算法(UCC-OCCF).首先,建立基于邻居的共同偏好表示...

One Class Collaborative Filtering Recommendation Algorithm Coupled with User Common Characteristics

Combining explicit features with implicit feedback is a common method to improve the recommendation accuracy of one class collaborative filtering(OCCF).However,current studies generally integrate the original explicit features or cross features directly into OCCF models,which makes it difficult to determine which explicit features are really vital,so it is untoward to achieve significant performance improvement.To sum up,a one class collaborative filtering recommendation algorithm coupled with user common characteristics(UCC-OCCF)is proposed.First,the neighbor-based common preference representation network(NB-CPR)is established to learn the interaction between users with similar explicit characteristics as the current users and a certain type of item,and to indirectly use explicit characteristics to obtain common preferences.Then,the deep latent factors representation(DLFR)uses a deep neural network to learn the potential factors between the user and the item,thus obtaining the interaction probability between the current user and the item.At last,the NB-CPR is combined with the personal depth latent factor representation network for training,so as to couple the common characteristics of users into OCCF model to improve the recommendation accuracy.Experimental results on public datasets MovieLens 100 K,MovieLens 1 M and MyAnimelist,show that UCC-OCCF can significantly improve the recommendation accuracy of OCCF.