# TRSDL: Tag-Aware Recommender System Based on Deep Learning-Intelligent Computing Systems
## 1 Introduction  
In the era of information overload, deriving effective content from large volumes of information for Internet users has become an urgent problem. Recommender systems (RS) are regarded as integral to solving this problem, as they efficiently analyze the interests, preferences and needs of individual users and then provide personalized services to them. Many techniques for generating personalized recommendations have been proposed. For rating prediction task, one of the branches of recommendation, traditional algorithms focus on users' ratings of items but do not take item properties or user evaluations into account. In such cases, tag-aware recommender systems (TRS) are used to improve performance. Tags provide valuable supplementary information for RS, as they summarize items' properties and reflect user preferences through tagging behaviors. While tags benefit recommender systems considerably, they also have unfavorable features. A tagging system allows users to tag items using arbitrary words or phrases, which creates two common issues: (1) redundancy, i.e., different users may use different tags to express similar concepts,such as \"pretty\" and \"beautiful\"; and (2) ambiguity, i.e., the same tags may have different meanings. For example, the word \"train\" has two meanings: a type of transportation or the teaching of skills through practice and instruction. These issues degrade the accuracy of TRS, as they prevent users from finding relevant information when ambiguous tags are used because common features are omitted when tags are represented in different ways. One traditional solution utilizes clustering in the tag space [1]. However, it is difficult to compute similarities between tags when tag space is very sparse. Another traditional solution computes the similarity between two users' tag sets by using the tool WordNet [2] to represent the similarity between the two users [3]. WordNet is an online English lexical database. It splits all entries into many groups according to their meanings. However, weaknesses of WordNet are obvious: it is time-consuming and labor-intensive to define the dictionary manually. Besides, words of the dictionary not only are limited, but also cannot keep up with the changes in language. In our proposed model, we use Word2 Vec [4], a tool for mapping words to \(K\)-dimensional distributed vectors, to represent tags with word embeddings. This method captures the semantic information of tags and simplifies the complex processing of text contents into the computing of vectors. At the same time, the weaknesses of tags are addressed because the distance of two vectors can be used to represent their semantic similarities between texts. On the other hand, deep learning has developed rapidly over the past decades and has achieved success in a variety of tasks, including those involving recommender systems [5]. Most machine learning algorithms used in traditional recommender systems are shallow structures that are limited in their use of complex data. The structures of deep neural networks render them better able to extract more abstract and representative latent features, improving the accuracy and generalization ability of learning models. In addition, consider a situation in one's daily life: after watching the movie entitled \"Cold War 1\", one is likely to search for the movie \"Cold War 2\" or for another [PERSON] movie to watch. According to this perspective, users' preferences evolve over time, and this evolving trajectory follows certain inner logic. Motivated by this, we organize the historical tagging records of target users by time, and then leverage recurrent neural networks (RNN) to extract users' hidden features to take full advantage of the time-sensitive preferences of users. Recently, deep models focus on hashtag recommendations [6; 7; 8] and tag-aware top-n recommendation [9; 10] are also proposed. However, as far as we know, research that apply sequential deep learning to tag-aware rating prediction remains rare, and there is room for improvement.