# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
###### Abstract  
This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address the existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post), with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to assess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4 Rec++TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAIL. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their potential for both academic and practical applications in the field. recommender system; session-aware recommendation; latent-context information; long-term and short-term preference; gated recurrent unit +
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limitation is its reliance on a narrow data range, leading to less personalized recommendations. It is, however, beneficial for users seeking recommendation system convenience without needing to register or log in. To facilitate personalized recommendations, the session-aware recommender system (SARS) was developed [7]. SARS involves recommendations comprising multiple sessions [8; 9; 10; 11]. Personalized recommendations typically rely on the user's current interaction to infer their short-term behavioral intentions. While short-term behaviors significantly influence future interests and are thus considered short-term preferences, sessions too distant from the short-term period are classified as long-term preferences. Users have both long-term and short-term preferences integrated into their overall intention preference for recommendations [1; 11]. This method's limitations include overlooking context information and a rigid definition of short-term preferences based on the last session, which can limit recommendation adaptability. The correlation between a product and the current product represents potential information that should be considered in understanding the real intentions of consumers. Additionally, previous studies have often limited the definition of short-term preferences to the last session [11]. This approach can lead to recommendations that are too rigid and inflexible. This rigidity arises because a session's definition is based on time intervals, as mentioned earlier. If a user's intentions span a period that extends beyond the confines of a single session, this behavior pattern may not accurately represent the user's true intentions. Our work leverages the session-aware recommender system (SARS) over the session-based recommender system (SBRS) to address two critical shortcomings: overlooking contextual information and rigid definitions of long-term and short-term preferences. In our approach, we focus on user-item interactions, such as clicks or purchases, represented by item IDs. These interactions inform our prediction model, which aims to identify the next item a user is likely to engage with. We consider contextual information, divided into intra-context (additional information accompanying interactions) and inter-context (information spanning current and past sessions). One challenge in this method is ensuring the relevance of contextual information to the sequence of interactions.