Research on the Recommendation System Combining Bi-LSTM and NCF Algorithms
Guangquan Li, Xu Zhang · 2023
In order to enhance the performance of recommendation systems and achieve more accurate personalized recommendations, this paper presents a novel recommender system design and implementation that combines two algorithms. The fusion of Bi-LSTM and NCF algorithms in the recommender system represents a new approach to personalized recommendation algorithms. The paper introduces the principles and characteristics of Bi- LSTM and NCF algorithms, and proposes a novel recommendation algorithm called BL-LSTM that combines the two. This algorithm leverages the bidirectional modeling advantage of the bi-directional Long Short-Term Memory (Bi-LSTM) network to uncover latent connections between users and items, thereby enhancing the system’s performance. Experimental results demonstrate that the proposed algorithm outperforms the NCF algorithm and the baseline Collaborative Filtering (CF) algorithm in terms of recommendation accuracy and recall, indicating its significant potential for widespread applications.