# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 1 Introduction  
Tag-aware recommender systems (TRS), also known as folksonomies, have been successfully applied in a wide range of web services and applications, such as books, music, movies, and videos. The core character of TRS is that users are encouraged to mark any items with arbitrary tags [1]. These user-defined tags are generally constituted by concise words or phrases, which can reflect the behavioral habits and preferences of target users. Meanwhile, items will be annotated with various tags defined by different users. Accordingly, these tags can also describe the characteristics of items from multiple angles. In this way, the implicit relationship between users and items is established through tags. More importantly, these tags contain more plentiful information than ratings and are more direct and feasible than reviews. The integration of social tagging information brings benefits to the recommender systems, but also results in many new issues to be processed. For instance, since any word or phrase can be taken as a tag, this inevitably causes redundancy and ambiguity in the tag potential space [2, 3]. To guarantee the recommendation performance, customization strategies should be taken into account to address the tagging information in a reasonable and effective way. At the early stage, many researchers have attempted to exploit traditional machine learning methods, including tensor-based techniques [4], clustering-based techniques [5], and graph-based techniques [6]. However, they are usually shallow structures, which are insufficient to learn the rich semantic information from various tags. Owing to its strong potential of feature extraction, deep learning has been widely used in various fields [7, 8]. In recent years, it has also been introduced into TRS to extract more useful tag-based features of users and items in recent years. For example, ACF [3] leverages the deep autoencoders to learn low-dimensional user features based on tags. DSPR [9] employs the multi-layer perceptron (MLP) to deal with tagging information for capturing more abstract user and item representation. TRSDL [10] even establishes two different deep architectures: a deep neural network for learning item features and a recurrent neural network for user features. Furthermore, TNAM [11] and AIRec [12] integrate the attention network to capture tag-based features representing multi-aspect user preferences or item characteristics. In these feature-based models, users and items are generally represented by the associated tags. However, the tagging behaviors are rather sparse in the real scenario, leading to performance degradation. To cope with this problem, TGCN [13] and LFGCF [14] adopt the graph convolutional network (GCN) to aggregate more information from multi-hop neighbors. Moreover, the topological structure can also be learned to further address the problem of ambiguity and redundancy in TRS. With the advancement of tag-aware recommendations, more complicated structures are established to boost the recommendation accuracy. In most cases, the accuracy is the only target or the dominant target to be optimized. However, an accurate recommendation cannot indicate a satisfactory one in real applications [15]. For example, a user who prefers rock music can easily obtain a recommendation list full of rock music to guarantee accuracy. Since this rock fan knows rock music well and has even heard most of it, this accurate recommendation may be not valuable. In addition, recommendations full of similar items will make users feel bored and dissatisfied. Reinforced through repeated exposure to similar contents, the users will be trapped in a narrow scope of topics, resulting in the echo chamber or filter bubble phenomenon [16]. A good recommender system should have the capability to identify items of interest that users cannot find themselves. Therefore, diversified recommendations, aiming at providing items with a variety of choices and novel experiences, are receiving increasing attention to break the echo chamber [17].