A face attribute based recommendation system via integrating denoising autoencoder and hash coding

Nowadays, with the rapid development of commerce, how to effectively improve the performance of an recommendation system has aroused great concern. However, traditional recommendation system requires users to log in their accounts, which brings poor user experience. This paper presents a novel recommendation system by using face recognition technologies to extract face attribute information as the input automatically. The system first obtains the user information of identity, gender, age, and then gets feedback by expression analysis. Based on the acquired face attributes, we propose to extract compact binary user features by integrating denoising autoencoder and hash coding, which can effectively improve the computing efficiency.The hash features from DAE-H-Face and DAE are further combined to enhance the representation ability. Finally, Hamming similarity-based collaborative filtering is used for recommendation. Experimental results on the MovieLens database show that the proposed recommendation method has better effectiveness and robustness. Moreover, the results also demonstrate its advantages to the cold start problem.

Automated summary

This study proposes a recommendation system based on face recognition technology, using hash coding and autoencoder to extract user features, and utilizes collaborative filtering for recommendation.The experimental results on the MovieLens database demonstrate that the method is effective and robust.