Existing machine learning approaches tend to focus

The gap

Evidence profile

Sourced from the future work and recommendations and stated research gap of the source papers, classified as general, spanning 5 journals.

Supporting evidence — 5 representative gaps

• Aspect-Based Deep Learning Model for Spam Review Detection (2026) · Research Digest on Engineering Management and Social Innovations · doi

  Although the proposed system demonstrates promising results in detecting fake reviews, there are several opportunities for further improvements and enhancements. One potential direction for future work is the integration of advanced deep learning models such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Transformer-based architectures. These models can capture deeper semantic relationships and contextual dependencies in review text, which may further improve detection accuracy. Another improvement could involve the incorporation of aspect-based sentiment analysis to analyze specific product features mentioned in reviews, such as price, quality, and delivery. This approach can provide more detailed insights into review authenticity by examining how users express opinions about different product aspects. Future systems may also integrate user behavioral analysis, including reviewer activity patterns, posting frequency, and rating behavior. Combining textual analysis with behavioral data can enhance the ability to detect suspicious review patterns. Additionally, the system can be extended to support multilingual review detection, allowing the detection of fake reviews written in different languages. This feature would make the system more applicable to global e-commerce platforms. Another possible improvement is the deployment of the model in large-scale cloud environments, enabling real-time analysis of massive volumes of reviews generated across multiple online marketplaces. Overall, future research can focus on combining advanced machine learning techniques, behavioral analysis, and large-scale deployment to build more intelligent and robust fake review detection systems.

  generalfuture workevidence 5/5
  Keywords: review reviews detection system fake future behavioral further advanced learning models based improvement product different
• Sentiment Analysis of Uber Customer Reviews Using Machine Learning and Deep Learning Techniques (2026) · International Journal of Innovative Research in Engineering · doi

  Although the proposed system achieves good performance, there are several areas for further improvement. Future research can focus on implementing advanced deep learning models such as BERT (Bidirectional Encoder Representations from Transformers) to achieve better contextual understanding and higher accuracy. Handling multilingual and code-mixed data is another important area, as customer reviews may contain multiple languages. Improving sarcasm detection is also necessary, as existing models may fail to correctly interpret sarcastic or ambiguous statements. In addition, implementing real-time sentiment analysis can enhance the system by enabling immediate processing of customer feedback. Furthermore, incorporating Explainable AI techniques such as LIME and SHAP can improve model transparency by explaining how predictions are made. Overall, future enhancements will focus on improving model accuracy, handling complex data, and making the system more efficient and reliable for real-world applications.

  generalfuture workevidence 5/5
  Keywords: system future focus implementing models accuracy handling customer improving real model proposed achieves good performance
• An Efficient Deep Learning Framework for Real-Time Product Recommendation in E-Commerce (2026) · The American Journal of Interdisciplinary Innovations and Research · doi

  algorithms that frequently encounter data sparsity and cold-start issues. The contextual significance of user evaluations is also not captured by them. Machine learning techniques make text more adaptable, but it's impossible for fully comprehend complex semantic patterns sequential presently. When modeling dependencies in evaluation data, deep learning, and LSTM networks in particular, perform better. Additionally, user evaluations include valuable sentiment data that is sometimes overlooked. As a result, there's an opportunity to enhance suggestion quality by merging deep learning with sentiment analysis. The need for more precise, personalized, individualized, and context-aware recommendation algorithms for modern e-commerce platforms have inspired the research. This paper's primary contributions are as follows: similarity are conventional • Introduces a sentiment-driven recommendation approach that improves personalization by leveraging user review polarity. • Enhances recommendation accuracy by capturing contextual meaning and sequential patterns in textual feedback using deep learning. • Improves learning from imbalanced e-commerce datasets, leading to more reliable and unbiased predictions. • Achieves consistently performance compared to existing ML and DL baselines across all evaluation metrics. superior • Strengthens real-time recommendation quality by linking user sentiment with product directly relevance. • Demonstrates high scalability and robustness for deployment in large-scale e-commerce environments. For e-commerce real-time product recommendation, the proposed strategy is innovative since it combines sentiment analysis with an LSTM-based DL model. Rather of relying on generic recommendations, it improves customization by using the emotion of user reviews. With the help of SMOTE, the model is able to handle class imbalance and accurately capture sequential textual patterns, resulting in more accurate predictions. Its better performance over conventional ML and DL approaches across all of the evaluation measures justifies its explanation. Its ability to provide scalable, accurate, and sentiment-aware product suggestions has been proven to greatly improve customer satisfaction. A. Structure of Paper The rest of the paper is organized as follows: Section II reviews the relevant literature. A thorough description of the recommended method is given in Section III. The experiments and their findings are presented in Section IV. Lastly, Section V concludes and outlines future directions. II. LITERATURE REVIEW The following sections include machine learning, product recommendation systems and a literature review on techniques and algorithms used to develop better recommendation systems.

  generalrecommendationsevidence 5/5
  Keywords: recommendation learning sentiment user commerce product algorithms patterns sequential evaluation deep better improves review literature
• Sequential Tourism Recommendation Using Dual-Input LSTM for Sustainable Destination Distribution (2026) · Teknika · doi

  lists. Experimental The proposed recommendation model is evaluated using Top-K Accuracy metrics, which are widely used in recommendation system research to measure the relevance of generated results demonstrate that the proposed dual-input LSTM architecture achieves strong predictive performance in identifying relevant next-destination recommendations from user travel histories. The evaluation results further indicate that integrating destination activity patterns significantly improves contextual recommendation quality compared with conventional recommendation approaches. Overall, this research contributes to the development of intelligent tourism recommendation systems by introducing a dual-input sequential prediction framework capable of modeling contextual travel behavior more effectively. The proposed approach advances the application of deep learning techniques in tourism informatics and demonstrates strong potential for supporting data-driven tourism exploration and personalized travel recommendation services. tourist and II. RESEARCH METHOD Figure 1. Research Stages Figure 1 illustrates the overall research methodology employed in this study for developing the predictive tourism recommendation system using a dual-input Long Short-Term Memory (LSTM) architecture. The research process begins with tourism destination identification and synthetic dataset construction, followed by data preprocessing and sequence transformation. Subsequently, the dual-input LSTM model is DOI: 10.34148/teknika.v15i2.1468 TEKNIKA, Volume 15(2), July 2026, pp. 244-251 ISSN 2549-8037, EISSN 2549-8045 246 Prasiwiningrum, E. et al.: Sequential Tourism Recommendation Using Dual-Input LSTM for Sustainable Destination Distribution developed and trained to learn sequential travel behavior patterns based on destination and activity sequences. Finally, the proposed model is evaluated using Top-K Accuracy metrics to assess its recommendation performance and predictive capability.

  generalrecommendationsevidence 5/5
  Keywords: recommendation tourism destination dual input proposed using lstm travel model predictive sequential evaluated accuracy metrics
• An Interpretable and Optimized Attention-Driven LSTM Framework for Email Sentiment Classification (2026) · F1000Research · doi

  Existing machine learning approaches tend to focus on incorporating dependencies between words, but do not take into account the context and sequence in the emails. The lack of a deep learning approach that can efficiently classify email sentiments. The need for a model that can provide valid explanations for its predictions.

  generalstated research gapevidence 5/5
  Keywords: existing machine learning approaches tend focus incorporating dependencies

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