# Logit Averaging: Capturing Global Relation for Session-Based Recommendation
## 1 Introduction
Even though their work was to implement the truly personalized session-based recommendation, we found this work has a limitation on data availability. Most of the existing session datasets do not contain user information including their user IDs. Since session-based recommender systems are proposed to make recommendations solely based on the sequence of items anonymous users interacted with within a limited period of time, HG-GNN can be utilized in very few circumstances where the user's ID and historical sessions are available. However, those attempts rather increase the computation costs in storing memory and extracting global-level item representation with over a million item nodes and fails to show drastic performance improvement. We propose a simple and novel algorithm called Logit Averaging (LA) that reflects global relations among sessions by averaging short-head item's logits which are the last representation of the model. There were several preceding studies using logit to improve representation learning in classification tasks such as logit adjustment. Adjusting logits encourages a large relative margin between logits of rare labels versus dominant ones. [PERSON] et al. [28] have conducted extensive studies on classical logit adjustment techniques and proposed a novel logit adjusted loss function. Even though the effectiveness of the logit adjustment loss function in balancing the class label during training, we found this technique hard to apply in session-based recommendation tasks. [PERSON] et al. utilized image datasets, such as CIFAR-10 and iNaturalist, to examine their proposed methods, which have only nine to ten classes. However, in session datasets, the class of each session is thousands of different last-clicked items. If we apply the logit adjustment loss technique to a session-based recommender system, the training of the model will not be performed properly. Thus, we propose a suitable logit training technique for a session-based recommender system called Logit Averaging. We perform experiments on multiple session-based recommendation models and verify that our algorithm outperforms in prediction accuracy and diversity. Our accomplishment in increasing the item diversity is especially important in recommender systems because increasing the probability of retrieving unusual or novel items relevant to users highly improves the users' satisfaction with the system [29; 30; 31; 32]. We summarize our contributions as follows:  
* By interpolating logits of the sessions with the same target value, we propose a simple yet effective method to incorporate global contexts for session-based recommendation and achieve high accuracy and diversity;
* We conduct extensive experiments on four real-world datasets in six different deep-learning-based session recommendation models and successfully demonstrate the effectiveness of our method;* We can easily \"plug and play\" Logit Averaging (LA) in various neural network-based session recommendation models. The structure of this paper is as follows. In Section 2, we define the problem and the corresponding assumption we made. Furthermore, an in-detail description of our proposed algorithm, Logit Averaging (LA), is mentioned. In Section 3, we describe experimental settings, including the datasets, baseline models, and evaluation metrics. Then, we make a detailed analysis of the experimental results. Finally, in Section 4, we summarize our proposed method and discuss the future works.