HAM:a deep collaborative rankingmethod incorporating textual information
Cheng-wei WANG , Teng-fei ZHOU , Chen CHEN , Tian-lei HU , Gang CHEN
Front. Inform. Technol. Electron. Eng ›› 2020, Vol. 21 ›› Issue (8) : 1206 -1216.
HAM:a deep collaborative rankingmethod 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 recoMmender (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.
Deep learning / Recommendation system / Highway network / Block coordinate descent
Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature
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