# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 2 Related Work  
In traditional recommendation systems, prevalent methods include POP (Most-Popular) and item-based nearest neighbor (Item-kNN) [12]. Subsequent developments have introduced methods like collaborative filtering (CF) [13], matrix factorization (MF) [14; 15], and Markov chain (MC) [8]. These methods utilize user-click data as the basis for recommendations, where clicks during browsing represent varying preferences or interests of users, to predict their next item of interest.  
Sequential recommendation, a primary branch of recommendation systems, is further divided into session-based and session-aware recommendations. HCA [1], a hierarchical neural network model, focuses on enhancing short-term interests by capturing the complex correlations between adjacent data within each time frame. As RNN tends to gradually forget past information due to long-term dependencies, this method helps the system retain information.  
GRU4 Rec [2], is a recommendation system model employing the RNN approach. This model continually learns from past features through RNN, combined with the timing of data clicks, to construct a highly effective recommendation method at that time. Compared to traditional methods, RNN adds a sequential consideration, with experimental results underscoring its effectiveness and establishing a neural methods' status in recommendation systems.  
Neural session-aware recommendation (NSAR) [9] operates under session-aware recommendation, incorporating all sessions into the model for learning and predicting the next item. This work discusses the timing of feature integration, positing that different integration opportunities significantly affect recommendation performance, hence proposing two strategies: pre-combine and post-combine.  
Figure 1: Long-term vs. short-term preferences by varying window scope.  
Inter-Intra RNN (II-RNN) [16] is a two-layer RNN architecture model which can effectively enhance recommendation performance and expedite feature learning. This is because the final prediction representation of the inner network is passed to the initial hidden state of the outer network. The outer network, thus, does not start learning from scratch, a method whose effectiveness is proven by this research. CAII [17] is another two-layer GRU model which utilizes session information, including item ID, image characteristics, and item price, to compare these features and achieve a balanced CAII. The CAII-P strategy emerged as the best solution in this research, suggesting that image features do not substantially enhance recommendation quality. This also indicates that session information is not directly correlated with sequential data, a key motivation for our study.  
The discussed research highlights that personalized recommendations cannot be solely based on session-based methods. Hence, session-aware recommendation research has been extended. Additionally, the use of latent-contextual information as an auxiliary method in recommendation systems has been explored but not extensively developed.  
Our work builds on session-aware recommendation, integrating latent-contextual information and long-term and short-term preference features. We adopt HCA's [1] approach of using GRU prediction representation as an auxiliary feature for model learning, treating such information as latent context. Drawing from NSAR's [9] pre-combine and post-combine feature combination strategies, we revised the attention-gate formula for feature fusion calculations. Our proposed method's main framework follows the inner and outer GRU architecture of II-RNN [16], with a significant difference; while II-RNN inputs only item IDs, our method also incorporates latent-contextual information and designs for processing long-term and short-term preferences.