# Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
## 1 Introduction  
Recommender systems, especially top-N recommender systems, play a key role in our daily life [1]. Traditional models of recommender systems recommend interest items to different users based on historical feedback information [2, 3] based on methods such as collaborative filtering [2]. Recently, more and more auxiliary data have also been applied in recommender systems for improving their performance [4, 5]. However, the complexity and heterogeneity of auxiliary data pose a big challenge for their use in recommender systems. Today, graph neural network (GNN)-based models [6, 7, 8, 9, 10] have achieved significant success in recommender systems. Specifically, in a GNN, first, the interactions of users, items, and auxiliary information are considered to be a graph structure. Then, the information representation of users and items is obtained with an information propagation mechanism. For example, SVD++ [6] enriches the representation of users by incorporating implicit feedback on items, in addition to explicit interactions. ItemRank [7] constructs anitem-to-item graph based on the interaction history, where the edges in the graph represent the similarity among items. It then uses a random walk algorithm to obtain the probability of a user visiting each item, which is used to rank the items. However, these networks cannot effectively capture complex heterogeneous information. Consequently, heterogeneous information networks (HINs) [11; 12], which include nodes and links, have been proposed for recommender systems due to their effective ability to acquire the semantic and representation information of nodes. The nodes in HINs include users and items, while links are meta-paths representing the relations among nodes. Many HIN-based methods [13; 14; 15] have been proposed, such as hierarchical attention network (HAN) [13] and meta-path-based context for top-N recommendation (MCRec) [16]. Although many existing models based on HINs for recommender systems have shown better performance due to their superior ability to process heterogeneous data and capture rich semantic information, they still face several problems. Firstly, although some researchers have focused on the explicit information of meta-paths, the proposed methods do not consider the temporal relations among different nodes in meta-paths [17]; this is mainly due to the fact that the static embedding methods used in such studies are subject to limitations and time dependency is often not taken into account. For example, users' browsing and purchasing behaviors on e-commerce platforms exhibit a certain order over time. The temporal relations among different nodes can help capture the sequence of these behaviors, thereby allowing for a better prediction of what content users may be interested in next. Secondly, although in HINs the relations between nodes are considered, the interactions between nodes and meta-paths are not accounted for [18]. Thirdly, HINs cannot effectively learn the heterogeneous information of nodes [16]. For example, a movie recommender system includes various entity types, such as users, movies, directors, and actors, and the relationships among them exhibit high heterogeneity. In view of these problems, a system for the temporal enhancement of top-N recommendations on HINs called TMRec is proposed in this study. It is mainly based on two architectures: long short-term memory (LSTM) [19], which can remember important information and forget irrelevant details by controlling the flow of information, and residual self-attention (RSA) [20], which can dynamically focus on the most important parts of information, rather than processing all input information uniformly. Therefore, we adopt these two methods for learning heterogeneous information among users or items, as well as effectively capturing the temporal relations among users or items.