# Logit Averaging: Capturing Global Relation for Session-Based Recommendation
## 1 Introduction  
With the rapid growth of Internet commerce platforms, recommender systems now play an imperative role in improving user experience and satisfaction on the platforms [1]. Predicting users' next behavior from current and previous actions not only alleviates information overload problems but can also offer a personalized service to targeted users in various applications [2]. A conventional recommender system [3; 4; 5; 6; 7; 8] utilizes a history of user-item interactions and side information to learn each user's long-term and static preferences on items. Interest-aware Message-Passing GCN (IMP-GCN) [8] was a model that plays to the strength of the Graph Convolution Network in recommender systems by alleviating the over-smoothing problem. [PERSON] et al. pinpointed the risk of involving higher-order neighboring users with no common interests with a user and learning their embeddings in the graph convolution operation. To overcome the addressed problem, IMP-GCN performed high-order graph convolution inside subgraphs that consist of users with similar features. We found this model very effective in its ability to avoid the propagation of non-related information from high-order neighbors into embedding learning. IMP-GCN was designed based on the user-item bipartite graph datasets, which consist of user and item features. However, this form of dataset is often invalid in many cases such as numerous anonymous users interacting with items in a very short period of time or the same account can be shared with multiple people [9]. Thus, a new system to deal with the problem has emerged, which is a session-based recommendation [10]. Unlike conventional ones, session-based recommender systems only utilize the ongoing movement of an anonymous user in one session to predict the next item, where a session is composed of items that users interactedwith within a continuous period of time. Owing to its practicality in various e-commerce platform scenarios, the session-based recommender system has gained much attention. To predict the users' next click items, the session-based recommender system utilizes a deep neural network and has achieved outstanding performances. Recurrent Neural Networks (RNNs) or attention mechanism models were first applied to a session-based recommendation task for extracting sequential patterns, which is a straightforward approach to handling a session as it is the sequence of items user clicked sorted by time [11; 12]. NARM [11] utilized gated recurrent unit (GRU) [13], which is the variation of RNN to extract sequential patterns in sessions. SRSAN [12] took dependencies among items in sessions by using self-attention networks. Furthermore, recent studies incorporate Graph Neural Networks (GNNs) [14] to construct a session-based recommender system due to its effectiveness in representing session consistency along with item dependencies and shows a competitive performance comparable to RNNs-based approaches [15; 16; 17; 18; 19]. SRGNN [15] was the first GNN-based framework and has been used as a basic structure in many studies. A session graph was sent into gated-GNN [20] to obtain the latent representation of nodes, and the session representation was generated by a soft-attention mechanism based on the node representations. [PERSON] et al. [16] normalized representations of items and sessions generated from GNNs to increase the overall recommendation performances. [PERSON] et al. [17] introduced a novel GNN model that focuses on aggregating the information passed from neighboring nodes using a GRU while preserving the item orders in sessions. This layer is called the Edge-Order Preserving Aggregation (EOPA) layer and shows a promising performance while minimizing the information loss problems. TAGNN++ [18] adaptively activated the attention model with respect to varied target items and improved the expressiveness of the model. Many researchers have tried to capture the accurate preference of an anonymous user from a short session, but it is still hard. To overcome this, [PERSON] et al.