## 5 Conclusions  
The session-based recommendation is a challenging task where recommendations are made based on sequences of items clicked by the user. In this paper, we proposed SkipGT, which is a novel skip-gram- and transformer-based model for session-based recommendation. During sessions, the time intervals between user interactions are ignored. In this case, it makes it difficult to understand higher-order item-item information. SkipGT mainly focuses on problems caused by a lack of user information, hidden behavioral information, or misclicking. In particular, SkipGT captures the user's different interactions and interest patterns. The contribution of a pre-trained model in session-based recommendation is demonstrated using skip-gram. Furthermore, SkipGT improved item representations by combining two models that capture item relationships in ordered arrays. Experimental results with three real datasets confirm that SkipGT outperforms all state-of-the-art baseline methods.  
In the future, we plan to investigate side information such as time and category to help improve the performance of session-based recommender systems. Also, we plan to integrate current methods in natural language processing and bioinformatics into recommendation systems. Another aspect is that session-based recommendation systems can be improved by incorporating location information in the time-series data stream. Such recommendation systems analyze the user's interactions at a particular time and place and provide personalized recommendations.  
Conceptualization, E.C. and S.I.O.; Methodology, E.C. and S.I.O.; Software, E.C.; Formal analysis, E.C. and S.I.O.; Validation, E.C. and S.I.O.; Investigation, S.I.O.; Resources, E.C. and S.I.O.; Data curation, E.C.; Writing--original draft preparation, E.C. and S.I.O.; Writing--review  
Figure 4: Effect of learning rate: (**a**) Effect of learning rate on the precision value for datasets. (**b**) Effect of learning rate on the mean reciprocal rank value for datasets.  
Figure 5: Effect of hidden layer number: (**a**) Effect of hidden number layers on the precision value for datasets. (**b**) Effect of hidden number layers on the mean reciprocal rank value for datasets.  
and editing, E.C. and S.I.O.; Visualization, E.C.; Supervision, S.I.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository. The original data presented in the study are openly available in [DIGINETICA] at [[[http://cikm2016.cs.iupui.edu/cikm-cup](http://cikm2016.cs.iupui.edu/cikm-cup)]([http://cikm2016.cs.iupui.edu/cikm-cup](http://cikm2016.cs.iupui.edu/cikm-cup)) or [[https://competitions.codalab.org/competitions/11161](https://competitions.codalab.org/competitions/11161)]([https://competitions.codalab.org/competitions/11161](https://competitions.codalab.org/competitions/11161)), accessed on 24 June 2024]. The original data presented in the study are openly available in [YOOCHOOSE] at [[[http://2015.recsyschallenge.com/challenge.html](http://2015.recsyschallenge.com/challenge.html)]([http://2015.recsyschallenge.com/challenge.html](http://2015.recsyschallenge.com/challenge.html)) or [[https://www.kaggle.com/datasets/chadgostopp/recsys-challenge-2015](https://www.kaggle.com/datasets/chadgostopp/recsys-challenge-2015)]([https://www.kaggle.com/datasets/chadgostopp/recsys-challenge-2015](https://www.kaggle.com/datasets/chadgostopp/recsys-challenge-2015)), accessed on 24 June 2024].  
The authors declare no conflicts of interest.