# TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning
## 1 Introduction
However, this will significantly increase the scale of learning parameters, causing computing and storage burden. In addition, as is known to us, the user's preference on a target item comes from the fact that certain characteristics of the item exactly match some specific interests of the user. Therefore, it is not suitable to compress all the diverse interests of a user into the same representation vector when estimating a candidate item, as not all features are equally useful. The useless features may even produce unnecessary noises and deteriorate the recommendation performance. In short, ingenious approaches that can differentiate the importance of different features are required to extract tag-based latent features. Furthermore, although deep networks can automatically learn more expressive feature representations, it is not easy to extract appropriate low-dimensional dense representations for users and items when the potential tag space is very sparse. As discussed in [11; 12; 13], both low-level and high-level feature interactions should play important roles for recommendation performance, since such interactions of features behind user preferences and item features are highly sophisticated. To process the above-mentioned issues, we develop a tag-aware recommendation algorithm based on attention learning (TRAL), which adopts the attention mechanism to discriminate the importance of different features from tag space. Firstly, we utilize the tag embedding technique to extract low-dimensional dense features from the user-tag matrix and item-tag matrix. Secondly, to acquire more abstract and effective representation vectors for each user or item, the attention-based pooling layer is employed to compress these features to a single representation. In this way, different features are assigned different weights according to their importance. Therefore, tag-based features can make different contributions to the final prediction. More importantly, the use of the attention mechanism means that the importance of different features can be automatically learned without any human domain knowledge. Thirdly, the extracted representation vectors for users and items are concatenated together to feed into a general MLP. The high-level feature interactions are, hence, further learned for improving the recommendation performance. In addition, to make full use of low-level feature interactions, the generalized linear model is also introduced. Finally, we combine the representation vectors obtained from the linear model and the depth model and input them into a common logic loss function for joint training. To sum up, the main contributions of our work are listed as follows:  
* We point out the limitation of using simple compression methods to obtain the fixed-length tag-based vector that represents multi-aspect user preferences or item features. To this end, we develop a new tag-aware recommendation algorithm which introduces the attention network to adaptively learn the importance of different features. * We combine a generalized linear model and a deep neural network so as to take advantages of both low-level and high-level feature interactions. * We perform extensive experiments on two real-world datasets, demonstrating the rationality and effectiveness of the proposed TRAL. The rest of this paper is organized as follows. Section 2 briefly summarizes the related work. Section 3 introduces some preliminaries. We elaborate on the proposed TRAL in Section 4 and conduct experiments in Section 5. Conclusions are given in Section 6.