1. Introduction

2. Background Study

3. Related Works

4. Proposed Approach

4.1. Phase I: Deep Learning Bi-GRU Model

This section encompasses the data preprocessing steps, including the word embedding process applied to the resulting data. Additionally, it outlines the structure of the Bi-GRU neural network layers.

Figure 2. Proposed CF System Architecture.

Figure 2. Proposed CF System Architecture.

4.1.1. Data Pre-Processing

The data preprocessing phase is a crucial step in the development of recommender systems that leverage text data. The goal of data preprocessing is to prepare the text data for analysis by removing noise and irrelevant information, and by ensuring that the data is consistent and well-formatted. The following are some of the most common data preprocessing techniques used in recommender systems:

• Tokenization: The process of breaking down text into individual words or tokens is known as tokenization. This is accomplished by splitting the text based on whitespace and punctuation marks.
• Stop word removal: These are the common words that do not add much meaning to the text. Hence, these words are typically removed from the text to improve the efficiency of the analysis.
• Stemming: The process of reducing words to their base or root form is known as stemming. This is achieved by removing prefixes and suffixes.
• Lemmatization: Lemmatization is a process of grouping words that have the same base or root form, regardless of their inflection. This is done by considering the contextual meaning of words.
• Case normalization: Case normalization is the process of converting all words to either lowercase or uppercase. This is done to ensure that the text is consistent and well-formatted.
• Removal of extra white spaces: Extra white spaces are any additional spaces that are present in the text. These spaces are typically removed to improve the readability of the text.
• Removal of URLs: URLs are web addresses that are typically used to link to external websites. These URLs are typically removed from the text to improve the efficiency of the analysis.
• Removal of hashtags: Hashtags are used to tag text with keywords. These hashtags are typically removed from the text to improve the readability of the text.
• Removal of date and time references: To improve the efficiency of the analysis, Date and time references are typically removed from the text.

By applying these preprocessing techniques, the text data can be cleaned and prepared for analysis. This can improve the accuracy and effectiveness of recommender systems by removing noise and irrelevant information, thereby ensuring data consistency and its proper formating.

4.1.2. Bi-GRU Model

Bidirectional Gated Recurrent Units (Bi-GRUs) are a powerful recurrent neural network (RNN) that excels in natural language processing tasks. They have unique ability to capture long-term dependencies in sequential data, that are crucial for understanding the intricate nuances present in the user review text. Bi-GRUs offer not only superior performance but also efficient training, thereby making them highly suitable for scaling recommender systems. Research conducted by [36] has demonstrated that Bi-GRUs outperform other types of RNNs, including LSTMs, particularly when they are dealing with user review text. This advantage stems from their remarkable capacity to comprehend long-range dependencies, which enables them to have a deeper understanding of the text and ultimately leading to a more precise and accurate recommendations. The specific architecture of the Bi-GRU layers is illustrated in Figure 3.

As illustrated in Figure 3, the Bi-GRUs are trained on the input sequence in reverse order. Therefore, the output of hidden layers at time t is determined based on the outputs of both the backward and forward layers hidden at times t and t-1, by Eq (8), Eq (9), Eq (10) below:

$${\overrightarrow{h}}_{t-1}=GRU(x_t,{\overrightarrow{h}}_{t-1})$$

(8)

$${\overleftarrow{h}}_{t-1}=GRU(x_t,{\overleftarrow{h}}_{t-1})$$

(9)

$$\overleftarrow{h}=w_t{\overrightarrow{h}}_t+v_t{\overleftarrow{h}}_t+b_t$$

(10)

The GRU units apply a nonlinear transformation to the word embedding vector. As a result, this word embedding vector is encoded into a corresponding GRU unit, which resides in the hidden layer state. Where ${\mathrm{w}}_{\mathrm{t}}$ and ${\mathrm{v}}_{\mathrm{t}}$ are the weights related to the forward hidden layer state ${\overrightarrow{\mathrm{h}}}_{\mathrm{t}}$ and the backward hidden state ${\overleftarrow{\mathrm{h}}}_{\mathrm{t}}$, correspondingly at time moment t.; ${\mathrm{b}}_{\mathrm{t}}$refers to the offset value at the time moment t.

• Review Word Embedding Layer: The Word Embedding Layer is a crucial component of neural networks used for representing words as vectors. These vectors typically have dimensions ranging from 100 to 300 and are trained on extensive text corpora. By capturing the semantic meaning of words, these vectors significantly enhance the accuracy of natural language processing tasks. In the proposed CF system, the Review Word Embedding Layer plays a pivotal role. It accepts a fixed sequence of 200 words as input and transforms each word into its corresponding 300-dimensional GloVe word vector [37]. These embedding vectors are subsequently fed into the Bi-GRU layers, allowing them to learn the long-term dependencies at each specific time step present in the user review text.
• Drop-out layer: The embedded resulting word vectors can be encoded either with numerical values associated with the input words or by using one-hot vectors that is, vectors with only one position filled with a value of one and the rest filled with zeros. However, an optimized embedding matrix can cause overfitting, which can be prevented by using dropout on one-hot vectors [38]. The 1D of spatial dropout layer with a dropout rate of 0.5 plays a role in preventing the activation functions from becoming strongly correlated, leading to better generalization, and preventing overfitting of the model.
• Context Attention layer: The context attention layer is applied to more effectively predict the rating class of a given review since words do not equally contribute to indicating the importance of meaning or the message of the theme. The attention layer obtains such words that significantly demonstrate the meaning of a sentence. This model utilizes the attention mechanisms as in [39]. This mechanism initiates a context vector, which can be viewed as a fixed query, that helps to extract the informative words. The context vector is randomly initialized and jointly learned with the rest of the attention layer weights, formally: Let H be a matrix composed of output vectors $[{\mathrm{h}}_1,{\mathrm{h}}_2,{\mathrm{h}}_3,\dots ,{\mathrm{h}}_{\mathrm{T}}]$ that are produced by the last BiGRUlayer and $\mathrm{T}$ is the length of the sentence. Context attention r of the sentence is determined by the weighted sum of the produced vectors as by Eqs. (11), (12) and (13),

  $$M=\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{h}\left(H\right)$$

  (11)

  $$\alpha =softmax\left({\mathcal{w}}^{T}M\right)$$

  (12)

  $$r=H{\alpha }^T$$

  (13)

  Where $\mathrm{H}\in {\mathbb{R}}^{\mathrm{d}\mathcal{w}\times \mathrm{T}}$, ${\mathrm{d}}^{\mathcal{w}}$ is the dimension of the word vectors, $\mathrm{w}$ is a trained parameter vector and ${\mathcal{w}}^{\mathrm{T}}$is the transpose. Hence the dimension of $\mathcal{w},\mathsf{\alpha },\mathrm{r}$ is, ${\mathrm{d}}^{\mathcal{w}}\mathrm{T},{\mathrm{d}}^{\mathcal{w}}$ separately. The output of this layer is calculated as by Eq (14),

  $$h^{\mathrm{*}}=\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{h}\left(r\right)$$

  (14)

• Concatenation layer: The concatenation layer performs global max pooling and global average pooling to generate one feature map for each class in the classification task. The use of global average pooling requires no parameter optimization, which helps prevent overfitting. Additionally, global average pooling ignores spatial information, making it more robust against spatial variations in the input data. After applying global max pooling and global average pooling to each Bi-GRU output, the resulting vectors are concatenated into a 1D array. Finally, this concatenated output is passed through a dense output layer with a sigmoid function to provide the rating prediction class.

4.2. Phase II: Recommender System

The user-based CF methodology is employed in the second phase of the recommender system. This phase aims to leverage the predicted rating results from the previous phase to calculate the similarity between users.

The cosine similarity metric, widely used in information retrieval and recommendation systems, is employed for this purpose, and is adopted to quantify the similarity between user vectors representing their rating patterns. Following the similarity calculation, the subsequent step involves identifying the most similar users, commonly known as nearest neighbors. These users exhibit comparable preferences and behaviors based on their historical rating data. The nearest-neighbor approach enables the recommender system to harness the collective wisdom of users who share similar tastes and preferences, facilitating the capture of valuable user insights.

Figure 3. The Structure of (BiGRU) layers.

Figure 3. The Structure of (BiGRU) layers.

Once the set of K's similar or like-minded users is determined, the final step entails generating the top-N recommended items for the target user. This is accomplished by considering the preferences and choices of the identified similar users and incorporating their historical rating data and preferences. The recommended items are selected based on the collective wisdom and experiences of these users, ensuring a personalized and relevant recommendation. By incorporating user-based CF in this manner, the recommender system effectively taps into the collective intelligence and preferences of similar users, thereby improving the precision and significance of the recommendations. Thus, this collaborative approach enables the whole system to leverage the shared behaviors and tastes of a community of users, providing tailored recommendations that align with individual user preferences.

5. Experiments

5.2. Datasets

Various collaborative filtering (CF) recommendation methods are evaluated using the standard Amazon dataset [40]. This dataset includes millions of user reviews and ratings on Amazon, spanning from May 1996 to October 2018 Various collaborative filtering (CF) recommendation methods are evaluated using the standard Amazon dataset [40] This dataset includes millions of user reviews and ratings on Amazon, spanning from May 1996 to October 2018[41]. The Amazon dataset comprises 24 product categories. Due to the vast size of the dataset, handling all categories is challenging. Therefore, three product categories from different domains with the largest number of users and reviews were selected to train, develop, and evaluate the proposed method. The chosen datasets are Electronics, CDs & Vinyl, and Movies & TV. These datasets include user profiles and item numerical ratings ranging from 1 to 5, along with the corresponding plaintext reviews. Table 1 summarizes the detailed statistics of these datasets after pre-processing

Table 1. Classification of Amazon Dataset Categories.

Table 1. Classification of Amazon Dataset Categories.

Dataset	User #	Item #	Reviews#	Sparsity
Electronics	1,92102	63,001	16,60038	98.62
CDs &Vinyl	7,5127	64,443	10,77845	97.77
Movies &TV	1,23633	50,052	16,65265

5.5. Results and Discussion

This section investigates the performance of various recommender system approaches on the Amazon Reviews datasets (Electronics, Movies & TV, CDs & Vinyl) characterized by high sparsity levels (above 97%). Sparsity refers to the proportion of missing entries in the user-item interaction matrix.

5.5.1. Performance Comparison on Sparse Data

In recommender systems, sparse data can pose challenges as traditional methods struggle to learn robust relationships between users and items. We employ Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as evaluation metrics to assess the accuracy of recommendations.

The performance of various recommender system approaches on the three datasets with varying sparsity levels is summarized in Table 2. Here, we focus on the MAE metric to illustrate the impact of sparsity.

The results presented in Table 2, focusing on MAE, reveal a clear trend across all datasets. Traditional collaborative filtering (CF) methods (SVD, NMFRS) consistently achieve higher MAE scores compared to deep learning-based approaches (CNN, Word2Vec, Bi-GRUCF). This suggests that regardless of the specific sparsity level (above 97% in all cases), CF methods struggle to capture the intricate relationships between users and items in sparse data scenarios. Deep learning methods, on the other hand, excel at extracting richer feature representations from limited data points, leading to recommendations with lower absolute errors (MAE).

As shown in Table 3, Bi-GRUCF consistently achieves the lowest Mean Absolute Error (MAE) across all datasets, outperforming traditional methods (SVD, NMFRS) by an average of 55.4% and deep learning approaches like CNN and Word2Vec by an average of 77.8%. This significant improvement suggests that Bi-GRUCF's ability to leverage user reviews through a deep learning architecture grants it a substantial advantage in sparse data scenarios.

Further strengthening this notion is the superior performance of the proposed Bi-GRUCF method. As shown in Table 4, Bi-GRUCF achieves the lowest MAE scores across all datasets, consistently outperforming other approaches. This significant improvement underscores the effectiveness of Bi-GRUCF in leveraging user reviews through a deep learning architecture. By incorporating textual information from reviews, Bi-GRUCF can potentially learn more nuanced user preferences and item characteristics, leading to more accurate recommendations, especially when dealing with limited data.

The high sparsity levels (above 97%) in all three datasets likely play a crucial role in the observed performance differences. Traditional CF methods, which rely primarily on user-item interaction data, might struggle to learn robust representations in such sparse environments. Deep learning approaches, like Bi-GRUCF, which can extract additional information from user reviews, might be more advantageous under these circumstances.

It's important to note the limitations of CNN and Word2Vec approaches included for comparison. As evidenced by the high MAE scores in Table 1, relying solely on item features (CNN) or word embeddings (Word2Vec) might not be as effective as incorporating user reviews through a sequential learning approach like Bi-GRUCF. CNNs might struggle to capture the sequential nature of user preferences within reviews, while Word2Vec, while effective for item relationships, might not explicitly model user behavior.

Table 5. MAE and RMSE results for the three CF models on three datasets.

Table 5. MAE and RMSE results for the three CF models on three datasets.

Dataset	Sparsity#	Method	MAE	RMSE
Electronics	98.62	SVD	0.88	1.22
		NMFRS	0.94	1.25
		CNN	1.72	2.27
		Word2Vec	1.71	2.26
		MCNN	1.71	2.26
		Bi-GRUCF	0.37	0.81
Movies &TV vedio	97.77	SVD	0.78	1.08
		NMFRS	0.79	1.07
		CNN	2.21	2.86
		Word2Vec	2.21	2.86
		CNN	2.2	2.86
		Bi-GRUCF	0.38	0.8
CDs & Vinyl media	97.3	SVD	0.71	1.01
		NMFRS	0.84	1.14
		CNN	1.86	1.82
		Word2Vec	1.86	1.81
		CNN	1.85	1.81
		Bi-GRUCF	0.42	0.84

5.5.2. Evaluation Using Classification Accuracy Metrics

As mentioned earlier, classification accuracy metrics like Recall, Precision, and F-measure provide a more user-centric indicator of recommendation quality compared to MAE and RMSE. Here, items with true numerical ratings above 3.5 are considered relevant, and those below 3.5 are considered non-relevant. These values serve as a general guideline for the following experiments.

In the context of recommendation systems, the Top-N recommendation task involves presenting users with a list of N items, where N represents the number of recommended items. The value of N is crucial as it determines the amount of information presented to users. Based on extensive experimentation, we observed that a value of N=20 consistently yielded the best results across all models and datasets. This suggests that users tend to focus on the top 20 recommendations, making it a critical benchmark for evaluating recommendation quality.

The detailed results of the evaluation for Recall, Precision, and F1-measure are presented in Tables 6, 7 and 8. As shown in the tables, the Bi-GRUCF model consistently outperforms all baseline approaches (SVD, NMFRS, Word2Vec, RNN, CNN) across all three datasets (Electronics, Movies & TV, CDs & Vinyl).

A notable observation is the consistent superiority of Bi-GRUCF across different recommendation list lengths (N). For instance, in the Electronics dataset (Table 4), Bi-GRUCF achieves a Recall of 97.31% at N=20, significantly higher than the closest competitor, RNN (92.64%). This indicates that Bi-GRUCF is more effective in retrieving relevant items for users in this domain, regardless of the number of recommendations presented. Similar improvements are observed for Precision (Table 3) and F1-measure (Table 5) across all datasets, highlighting the overall superiority of the Bi-GRUCF model in terms of recommendation accuracy.

Table 9. Comparison of the Precision obtained on 3 datasets.

Table 9. Comparison of the Precision obtained on 3 datasets.

Dataset	SVD	NMFRS	Word2Vec	RNN	CNN	BI-GRUCF
Electronics	83.25	81.79	83.14	92.64	93.28	97.31
Movies & TV	87.16	81.88	87.05	90.7	92.39	96.82
CDs & Vinyl	84.71	82.28	84.6	90.89	92.65	95.95

Table 10. Comparison of the Recall obtained on 3 datasets.

Table 10. Comparison of the Recall obtained on 3 datasets.

Dataset	SVD	NMFRS	Word2Vec	RNN	CNN	BI-GRUCF
Electronics	84.84	83.52	84.73	93.11	93.44	95.04
Movies & TV	87.72	85.02	87.61	92.79	93.46	94.91
CDs & Vinyl	85.59	83.8	85.48	92.9	92.65	94.05

Table 11. Comparison of the F1-Meausre obtained on 3 datasets.

Table 11. Comparison of the F1-Meausre obtained on 3 datasets.

Dataset	SVD	NMFRS	Word2Vec	RNN	CNN	BI-GRUCF
Electronics	86.49	85.32	86.38	93.23	93.46	92.87
Movies & TV	88.28	88.41	87.05	93.62	93.56	93.08
CDs & Vinyl	86.49	85.37	85.48	92.41	92.4	92.23

The superior performance of Bi-GRUCF stems from its unique architecture, which effectively captures sequential information and context from user reviews. This is achieved through two key components: GRUs and an attention mechanism. GRUs selectively remember or forget information from the sequence, allowing Bi-GRUCF to capture long-term dependencies within user reviews better than RNNs. This leads to a deeper understanding of user preferences and context. Additionally, the attention mechanism focuses on the most informative parts of reviews by dynamically assigning weights to different words or phrases. This selective focus, in contrast to RNNs that process the entire sequence uniformly, enables Bi-GRUCF to extract more meaningful insights from user reviews. Consequently, Bi-GRUCF achieves superior Recall, Precision, and F1-measure across diverse datasets.

Table 6. List of Symbols.

Table 6. List of Symbols.

Symbol	Description	Category
R	User-item rating matrix	Variable
U	Set of registered users	Set
I	Set of all possible items	Set
u	User ID	Variable
i	Item ID	Variable
r_ui	Rating of item i by user u	Variable
N(u)	Neighborhood of user u (similar users)	Set
K	Number of nearest neighbors	Variable
pred_rui	Predicted rating of item i for user u	Variable
W	Weight matrix in Bi-GRU layer	Variable
b	Bias vector in Bi-GRU layer	Variable
H	Hidden state vector in Bi-GRU layer	Variable
x(t)	Input word vector at time step t	Variable
f	Forget gate function in GRU unit	Function
i	Input gate function in GRU unit	Function
o	Output gate function in GRU unit	Function
σ	Sigmoid activation function	Function
c(t)	Cell state vector in GRU unit at time step t	Variable
γ	Context vector in Attention layer	Variable
α(t)	Attention weight at time step t	Variable
φ	Embedding function (maps item/user ID to embedding vector)	Function

6. Conclusions

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