One Class Collaborative Filtering Recommendation Algorithm Coupled with User Common Characteristics

Abstract

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 100K, MovieLens 1M and MyAnimelist, show that UCC-OCCF can significantly improve the recommendation accuracy of OCCF.

Keywords

• |one-class collaborative filtering (occf)|deep learning|common preferences|implicit feedback|explicit feature