# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 1 Introduction
The recommendation diversity is useful to individual users as well as businesses. Users can access a wider range of contents and find more interest and novel things. For the e-commerce enterprises, diverse recommendations will increase their product sales and promote user satisfaction and user loyalty [18]. However, the improvement of recommendation diversity is inevitably at the cost of the decrease of accuracy. Generally speaking, it is easy to promote diversity by recommending unpopular items selected randomly, which will greatly reduce the accuracy. Therefore, the core of diversified recommendation is to trade-off the accuracy and diversity. Early works often utilize a two-stage scheme, which first adopts an accuracy-focused approach to yield the candidate item set and then selects a list of items by a re-ranking or post-processing strategy, such as clustering [19], MMR [20, 21] and DPP [22]. Since the candidate generation and post-processing are two independent processes, the diversified strategy is decoupled from the model optimization. The recommendation performance will mainly depend on the user and item representations learned in the upstream matching process. It is uncertain whether the decoupling design can diversify the recommendation with acceptable accuracy loss [23]. For this reason, recent works have turned to constructing an end-to-end (one-stage) supervised learning framework to improve diversity [23, 24, 25]. In this work, we focus on providing diversified tag-aware recommendations with minimal costs on accuracy. To this end, we propose a diversified tag-aware recommendation algorithm based on graph collaborative filtering (DTGCF). We establish a tag-aware GCN to learn representation of users, items, and tags. The advantage of the proposed GCN is two-fold. On the one hand, it can combine the node features of neighbors and topological structures to generate more accurate and reasonable node representations, thus improving the recommendation accuracy. On the other hand, more diverse items are able to be exposed to users due to the inherent high-order connectivity in the constructed graph, which can boost the recommendation diversity. Specifically, we first transform the user-item-tagtensor into two graphs: user-tag graph and item-tag graph. Inspired by LightGCN [26], we then establish a light tag-aware GCN to obtain node representations from the constructed two graphs. To further improve the accuracy, we employ a two-way TransTag regularization to model the relationship among users, items, and tags. To promote the recommendation diversity, we design personalized category-boosted negative sampling to choose a certain proportion of similar but negative items as the negative training set, where the probability of selecting these items for each user is adaptively determined by his propensity to diversity. In addition, we adopt adversarial learning to make the item representation category-free in the learned embedding space. In this way, items from diverse categories are more likely to be recommended. Combining these strategies, the proposed DTGCF can provide recommendations with respect to accuracy and diversity within an end-to-end framework. The main contributions of our work are given as follows:  
* We propose a novel diversified tag-aware recommendation model based on GCN, which combines the optimization process and the diversified strategies within an end-to-end framework;
* To balance the recommendation accuracy and diversity, we elaborately design three modules. Among them, personalized category-boosted negative sampling and adversarial learning are tailored for promoting diversity, while the two-way TransTag regularization function is well-designed for accuracy;
* We conduct extensive experiments on real-world datasets to show the superiority of the proposed DTGCF on diversified tag-aware recommendation. The remainder of this paper is organized as follows: Section 2 briefly reviews the related work. Some preliminaries are presented in Section 3. Section 4 details the proposed DTGCF.