# Logit Averaging: Capturing Global Relation for Session-Based Recommendation
## 1 Introduction
[19] applied disentangle representation learning to make better item representation. They assumed that the chunks of item embeddings represent factors such as categories and colors, and learned item embeddings with a distance correlation function aiming for the factors to disentangle each other. It is useful for the session-based recommendation task because of the absence of additional information in a session dataset. However, existing GNN-based models only make predictions with items in a single session and do not consider the global-level contexts that exist between inter-sessions. They ignore the useful item transition information from other sessions. In [9], the session-based recommendation has several unique characteristics: Session consistency, Repeated item consumption, Timeliness of sessions, and Sequential dependency. Here, we mainly take sequential dependency into consideration. Some items can be explored in a certain order from the majority of users. For example, we can easily find the item-transition information, ['phone', 'phone case'] or ['phone', 'wireless earphone']. This pattern will appear in multiple sessions. Thus, taking the global-level context of items into consideration is necessary to improve the prediction accuracy and diversify the recommendation lists. In addition, the number of items in a session is lower than 10 generally. The short length of sessions may lead to incorrect predictions when we make a prediction based on a single session. Therefore, it will be helpful to recommend items with global relations from multiple sessions which share the same items. Some studies are trying to model the global relations of sessions. [PERSON] et al. [21] applied collaborative neighborhood information to recommend items. It consists of an inner memory encoder and an outer memory encoder that extract the current session's context and the collaborative information from other sessions which are similar to the current session, respectively. [PERSON] et al. [22] conducted a similar attempt to infer global context through the GCE-GNN (Global Context Enhanced Graph Neural Networks) model. It constructed a global graph using all the unique items that exist in inter-sessions and extracted the global-level item embedding. [PERSON] et al. [23] proposed MAN (Memory Augmented Model) for a session-based recommendation, which augments a base recommender model using updated memory, and recommends items with memory components. [PERSON] et al. [24] utilized a graph including item relations of each day to capture timely user interests. By doing this, an item can have different neighborhoods on different days. [PERSON] et al. [25] tried to model inter- and intra- item transition through multi-task learning. Each session went into the self-attention layer to capture intra-sessions item transition, and item representations which are constructed at this step were set as initialized values of item representations for learning global item relations. They could improve the recommendation accuracy by designing different layer architectures depending on whether the item is from global or local relations. [PERSON] et al. [26] also tried to reflect the global relation among sessions. They proposed two different graph view augmentations with respect to a session dataset to adopt the self-supervised co-training method on a session-based recommendation. One is the item view graph, generally utilized in session-based recommendation, and the other is the session view graph which represents the inter-session relationship with Jaccard similarity. By learning from these two views, items and sessions, session representations became richer. The Heterogeneous Global Graph Neural Network (HG-GNN) [27] was another model that attempts to extract global item co-occurrence by constructing user-item interactions and global co-occurrence item graphs. They used heterogeneous global graphs to learn long-term user preferences and item representations.