Improved one-class collaborative filtering for online recommendation

Recommended systems are becoming important and popular for online shopping platforms and vendors. Moreover, one-class collaborative filtering, which is based on the modeling of the feedback records of E-commercial website consumers, is one of the most widely used recommendation algorithms both academically and practically. However, one significant drawback of the existing techniques is that they typically do nor consider ratings and visual-temporal contexts, which are useful and important in modeling user behaviors. Therefore, to address this problem, we propose a new recommendation algorithm which is based on the combination of image features, user feedback ratings, and product evolution trends. The image feature can be extracted automatically using deep convolution neural network. Thus, our technique, which is essentially a time-aware visual mode, can represent the different visual feature preference of users over time. Our model is evaluated using the widely adopted Amazon online data and shown significantly improvements.