Frontiers of Information Technology & Electronic Engineering
>> 2020,
Volume 21,
Issue 8
doi:
10.1631/FITEE.1900382
HAM: a deep collaborative ranking method incorporating textual information
Affiliation(s): The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province, Hangzhou 310027, China; State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310027, China; College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; less
Received: 0201-07-28
Accepted: 2020-08-10
Available online: 2020-08-10
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Abstract
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 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.
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