HAM:a deep collaborative ranking method incorporating textual information

The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual descriptions.It is highly desirable to explore a very deep neural network to capture the complicated nonlinear preferences.However,training a deeper recommender is not as effortless as simply adding layers.A deeper recommender suffers from the gradient vanishing/exploding issue and cannot be easily trained by gradient-based methods.Moreover,textual descriptions probably contain noisy word sequences.Directly extracting feature vectors from them can harm the recommender’s performance.To overcome these difficulties,we propose a new recommendation method named the HighwAy reco Mmender(HAM).HAM explores a highway mechanism to make gradient-based training methods stable.A multi-head attention mechanism is devised to automatically denoise textual information.Moreover,a block coordinate descent method is devised to train a deep neural recommender.Empirical studies show that the proposed method outperforms state-of-the-art methods significantly in terms of accuracy.