# Logit Averaging: Capturing Global Relation for Session-Based Recommendation
## 4 Discussion  
The recommender system is an essential part of an e-commerce platform to improve user experience and satisfaction. Most conventional recommendation models utilized user and item interaction information and side-information such as users' explicit feedback and user profiles [3; 4; 5; 6; 7; 8]. However, in many cases, users look around internet shopping websites without logging in and some users share the same account even if they logged in. Thus, the session-based recommender system, which utilizes anonymous session sequences to recommend items, has gained much attention. Session-based recommendation aims to predict an anonymous user's next action, whether she or he is likely to purchase based on the user's clicked-item in the current session, as sequences. Most recent research on session-based recommendations makes predictions solely based on a single session without incorporating global relationships between sessions [15; 16; 17; 18; 19; 22]. We argue that this method does not guarantee better performance because item embeddings learned by utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session item transitions simultaneously. Some existing methods [21; 22; 23; 24; 25; 26] tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, they still carry limitations in terms of computation cost. We proposed a novel yet simple algorithm called Logit Averaging (LA), helping to learn both local-level logits which come from intra-sessions' item transitions and global-level logits, which come from gathered logits of related sessions. By considering the long-tail item distribution of session datasets, we stored and aggregated the logits of sessions with target items that the majority of other sessions shared. Then, we applied the mean value of logits as a global context that a single session might miss. Extensive experiments verified that the baseline models with our proposed method, LA (Logit Averaging), outperformed the baseline models. The total gains of LA with baselines on the baselines are all larger than zero, so we demonstrated that the LA module helps to increase both recommendation accuracy and diversity. Addtionally, we conducted additional experiments to confirm the improvement of the proposed methods with existing augmentation methods. The baseline methods with LA and GraphMix [36] showed the largest increment for all metrics. Although we proposed the novel and simple method to overcome the challenges where session-based recommendation employs global item relations among the sessions, there are still a few limitations. In [9; 24], sessions have temporal features, and user and items interactions and sessions represent consumption trends. Therefore, considering the temporal feature of sessions while collecting the global item transitions remains for our future work. Besides, the complex inter-relationships between sessions are still not entirely understood, and we think this is a fertile space for future exploration. Conceptualization, H.Y. and G.K.; methodology, H.Y. and G.K.; software, H.Y. and G.K.; formal analysis, H.Y. and G.K. and J.-H.L. ; investigation, H.Y. and G.K.; data curation, H.Y. and G.K.; writing--original draft preparation, H.Y. and G.K.; writing--review and editing, H.Y. and G.K.; visualization, H.Y. ; supervision, J.-H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program IITP-2021-2020-0-01821 and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00421, Artificial Intelligence Graduate School Program (Sungkyunkwan University). Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study.