## 1 Introduction
Second, sessions with identical interaction structures but different click orders are often undetected by GNN-based methods. Third, users' interest over short periods suggests significant time sensitivity, making time intervals a critical marker in determining user interests. Although extensive research has been conducted on session-based recommendation systems, user behavior patterns within session sequences have not been comprehensively examined. It has been noted that the user's primary goal in the current session sequence is often to find the next satisfying item with an exploratory approach. Users may sometimes click on the same item more than once. The user's click sequence can range from extensive research to individual product comparisons, viewed as a long-term activity. In this process, it becomes clear that not all items in the click sequence directly contribute to the user's decision-making. Hence, there is a need to model user behavior in more detail. Briefly, we highlight the main challenges encountered in session-based recommendation (SBR) systems as follows: (1) How to explicitly account for the impact of time intervals on the user's long-term and short-term preferences. (2) How to model the user's active exploration behavior throughout a session without disrupting the temporal order of clicked items. (3) How can these behavioral patterns be simulated from an appropriate perspective and emphasized in terms of next-click prediction, considering the unequal importance users attach to each item during the product research or comparison process? (4) How to represent instances in the session data where the user does not make any purchases or makes incorrect clicks. In order to overcome the above-mentioned challenges and improve the session-based recommendation, we propose the SkipGT model, which combines skip-gram and transformer models. First we applied the skip-gram model to session data and create the item embeddings. Then the item embeddings were given to a transformer model. In our study, the skip-gram model which was used to predict the context items of a specific target item effectively captures representation relationships in a dense vector space where each item is represented as a vector. On the other hand, the transformer model is designed in a non-iterative structure to process sequential data. The applied transformer leverages self-attention mechanisms to evaluate the importance of items in a session regardless of their spatial distance, allowing it to handle context and connections in data more effectively than previous models. Where existing methods fall short, we improve performance in the recommender system by using skip-gram to model the context of an item in the session and the transformer method that takes into account the long-term dependencies of items in the session. We have conducted comprehensive experiments on two real-world datasets involving retail sales rep sessions and browsing records. These experiments demonstrate that our proposed SkipGT model significantly outperforms existing state-of-the-art solutions. Consequently, the main contributions of our work are summarized as follows:  
* We proposed the SkipGT model to identify users' global and current interests, behavioral patterns, and critical preferences. * We utilized skip-gram pre-training for the first time to generate session vector embeddings in session-based recommender systems. * To the best of our knowledge, this is the first model in session-based recommendation systems that combines the skip-gram model and transformer in natural language processing. * We introduced a model that captures bidirectional conversions in session-based sub-reddits and represents users with different interest patterns. * The proposed model's efficacy is substantiated through rigorous experimentation, where it is compared against contemporary benchmark models across three distinct datasets. The rest of the paper is organized as follows. In Section 2, we present the literature review of session-based recommendation studies.