Enhancing Sequential Recommendations with Bi-Directional Models: A Comparative Analysis

In the online world, recommendation systems are crucial for enhancing the user experience by suggesting relevant items by utilizing user actions. However, they face significant challenges in adapting to evolving user preferences and handling dynamic content. Traditional recommendation algorithms struggle with these challenges, particularly in recognizing evolving user preferences over time. Moreover, they may not effectively handle large datasets or capture intricate patterns in user behavior. To address these challenges, this study analyses the effectiveness of bidirectional models, focusing specifically on Sequential Recommendations with Bi-LSTM (SRec with Bi-LSTM). Unlike traditional static systems, Bi-LSTM offers dynamic and personalized content suggestions by analyzing user actions bi-directionally. This approach enables the system to capture complex patterns in user-item interactions and adapt to changing user preferences over time. In this study, a comparison is made between the performance of Bi-LSTM with several recent techniques, such as Simple RNN with LSTM, Gated Recurrent Unit (GRU) with LSTM, CUDA Deep Neural Network Long Short-Term Memory (CuDNNLSTM), and Stacked RNN. The analysis demonstrates that Bi-LSTM consistently outperforms these methods in terms of accuracy and adaptability to evolving user preferences. By addressing the challenges faced by traditional systems, the proposed Bi-LSTM model offers a promising advancement in personalized sequential recommendations, thereby improving the overall user experience in the fast-paced and ever-changing landscape of online content.