A Recommender Framework for BP Neural Networks with an Attention Mechanism

A BP Neural Network Based Recommender Framework with Attention Mechanism

ABSTRACT:

Recommender systems have been enhanced with deep neural networks (DNNs) to improve prediction accuracy through nonlinear representation learning. However, DNNs often come with high computational and storage costs, and they can suffer from overfitting due to limited available ratings. In this article, we introduce a novel recommendation framework called BPAM++ (Back Propagation neural network with Attention Mechanism) to address these challenges. BP neural networks are utilized to capture the intricate relationships between target users and their neighbors, as well as between target items and their neighbors. By employing a shallow neural network instead of DNNs, the computational and storage costs are reduced, and overfitting issues caused by limited ratings are mitigated. Additionally, an attention mechanism is incorporated to capture the global impact of the nearest users on their respective target user sets. Extensive experiments conducted on eight benchmark datasets validate the effectiveness of the proposed BPAM++ model.

Introduction:

Recommender systems play a crucial role in facilitating personalized user experiences by suggesting relevant items or content. In recent years, deep neural networks (DNNs) have gained popularity in recommender systems due to their ability to learn nonlinear representations and extract complex patterns from data. However, the computational and storage costs associated with DNNs can be prohibitive, and the overfitting problem arises when the number of available ratings is relatively small.

To address these challenges, we propose a novel recommender framework called BPAM++ that leverages a Back Propagation (BP) neural network with an attention mechanism. By using a shallow neural network instead of DNNs, BPAM++ significantly reduces computational and storage costs while also mitigating overfitting issues caused by limited rating data. The attention mechanism enhances the framework by capturing the global impact of the nearest users on their respective target user sets.

BP Neural Network and Attention Mechanism:

The BP neural network is employed in BPAM++ to model the complex relationships between the target user and their neighbors, as well as the target item and its neighbors. Unlike DNNs, BP neural networks consist of only a few hidden layers, resulting in reduced computational complexity and storage requirements. This makes them more suitable for practical recommender systems, especially in scenarios where large-scale datasets are involved.

Moreover, an attention mechanism is incorporated into BPAM++ to capture the global influence of the nearest users on their respective target user sets. This attention mechanism assigns different weights to the nearest users based on their relevance, allowing the framework to focus on the most influential users during recommendation generation. By considering the global impact, BPAM++ can provide more accurate and personalized recommendations.

Benefits of BPAM++:

The proposed BPAM++ framework offers several advantages over existing approaches:

1. Reduced computational and storage costs: By employing a shallow neural network instead of DNNs, BPAM++ significantly reduces the computational and storage requirements, making it more feasible for real-world recommender systems.
2. Alleviation of overfitting issues: Limited rating data often leads to overfitting problems in DNNs. BPAM++ mitigates this issue by using a BP neural network, which is less prone to overfitting due to its simpler architecture.
3. Attention mechanism for global impact: The attention mechanism in BPAM++ captures the global influence of the nearest users on their target user sets. This ensures that the most relevant users are given higher weights, resulting in more accurate and personalized recommendations.

Experimental Validation:

To evaluate the effectiveness of the proposed BPAM++ framework, extensive experiments were conducted on eight benchmark datasets. The performance of BPAM++ was compared against other state-of-the-art recommender systems, including DNN-based models. The experimental results demonstrated the superior recommendation accuracy and efficiency of BPAM++

compared to the existing approaches. BPAM++ achieved higher precision, recall, and F1-score, indicating its ability to generate more accurate recommendations.

Furthermore, the computational efficiency of BPAM++ was evaluated by measuring the training and inference times. The results showed that BPAM++ significantly reduced the computational overhead compared to DNN-based models, making it more suitable for real-time recommendation scenarios.

The experiments also verified the effectiveness of the attention mechanism incorporated in BPAM++. By considering the global impact of the nearest users, BPAM++ outperformed the baseline models in capturing user preferences and generating personalized recommendations. The attention mechanism provided a fine-grained understanding of user-item interactions, resulting in improved recommendation quality.

Overall, the experimental results validated the effectiveness and efficiency of the proposed BPAM++ framework. By combining the advantages of a BP neural network and an attention mechanism, BPAM++ offers a promising solution to address the challenges faced by traditional DNN-based recommender systems.

Conclusion:

In this article, we introduced a novel recommender framework, BPAM++, which combines a BP neural network with an attention mechanism. The proposed framework addresses the limitations of DNN-based recommender systems, such as high computational and storage costs and the overfitting issue caused by limited rating data. By utilizing a shallow neural network and incorporating an attention mechanism, BPAM++ achieves improved recommendation accuracy, reduced computational complexity, and better capture of user preferences.

The experimental results on benchmark datasets demonstrated the superior performance of BPAM++ compared to existing approaches. The framework’s ability to reduce computational and storage costs, mitigate overfitting, and capture the global impact of nearest users showcased its effectiveness and efficiency.

Future research directions may include exploring the integration of other advanced techniques, such as graph neural networks or reinforcement learning, to further enhance the recommendation capabilities of BPAM++. Additionally, investigating the framework’s performance on larger-scale datasets and in real-world applications would provide valuable insights for its practical deployment.

In conclusion, the BP Neural Network Based Recommender Framework with Attention Mechanism, BPAM++, presents a promising solution for generating accurate and personalized recommendations while addressing the computational and storage challenges faced by traditional DNN-based recommender systems. With its potential to revolutionize recommender systems, BPAM++ opens up new avenues for research and applications in the field of personalized recommendation systems.

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