Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs
In the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enhance personalization. However, existing models stil...
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MDPI AG
2025-05-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/5/124 |
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| author | Zeinab Shahbazi Rezvan Jalali Zahra Shahbazi |
| author_facet | Zeinab Shahbazi Rezvan Jalali Zahra Shahbazi |
| author_sort | Zeinab Shahbazi |
| collection | DOAJ |
| description | In the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enhance personalization. However, existing models still struggle to adapt dynamically to users’ evolving interests across multiple content domains in real-time. To address this gap, the cross-domain adaptive recommendation system (CDARS) is proposed, which integrates real-time behavioral tracking with multi-domain knowledge graphs to refine user preference modeling continuously. Unlike conventional methods that rely on static or historical data, CDARS dynamically adjusts its recommendation strategies based on contextual factors such as real-time engagement, sentiment fluctuations, and implicit preference drifts. Furthermore, a novel explainable adaptive learning (EAL) module was introduced, providing transparent insights into recommendations’ evolving nature, thereby improving user trust and system interpretability. To enable such real-time adaptability, CDARS incorporates multimodal sentiment analysis of user-generated content, behavioral pattern mining (e.g., click timing, revisit frequency), and learning trajectory modeling through time-aware embeddings and incremental updates of user representations. These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. Our experimental results on datasets spanning social media, e-commerce, and entertainment domains demonstrate that CDARS significantly enhances recommendation relevance, achieving an average improvement of 7.8% in click-through rate (CTR) and 8.3% in user engagement compared to state-of-the-art models. This research presents a paradigm shift toward truly dynamic and explainable recommendation systems, creating a way for more personalized and user-centric experiences in the digital landscape. |
| format | Article |
| id | doaj-art-1ca9f6ce6bb24521acfd906471cc61a3 |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Big Data and Cognitive Computing |
| spelling | doaj-art-1ca9f6ce6bb24521acfd906471cc61a32025-08-20T02:33:38ZengMDPI AGBig Data and Cognitive Computing2504-22892025-05-019512410.3390/bdcc9050124Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge GraphsZeinab Shahbazi0Rezvan Jalali1Zahra Shahbazi2Research Environment of Computer Science (RECS), Kristianstad University, 291 39 Kristianstad, SwedenDepartment of Computer and Systems Science, Stockholm University, 106 91 Stockholm, SwedenDepartment of Environmental Engineering, University of Padova, 35122 Padova, ItalyIn the era of information explosion, recommendation systems play a crucial role in filtering vast amounts of content for users. Traditional recommendation models leverage knowledge graphs, sentiment analysis, social capital, and generative AI to enhance personalization. However, existing models still struggle to adapt dynamically to users’ evolving interests across multiple content domains in real-time. To address this gap, the cross-domain adaptive recommendation system (CDARS) is proposed, which integrates real-time behavioral tracking with multi-domain knowledge graphs to refine user preference modeling continuously. Unlike conventional methods that rely on static or historical data, CDARS dynamically adjusts its recommendation strategies based on contextual factors such as real-time engagement, sentiment fluctuations, and implicit preference drifts. Furthermore, a novel explainable adaptive learning (EAL) module was introduced, providing transparent insights into recommendations’ evolving nature, thereby improving user trust and system interpretability. To enable such real-time adaptability, CDARS incorporates multimodal sentiment analysis of user-generated content, behavioral pattern mining (e.g., click timing, revisit frequency), and learning trajectory modeling through time-aware embeddings and incremental updates of user representations. These dynamic signals are mapped into evolving knowledge graphs, forming continuously updated learning charts that drive more context-aware and emotionally intelligent recommendations. Our experimental results on datasets spanning social media, e-commerce, and entertainment domains demonstrate that CDARS significantly enhances recommendation relevance, achieving an average improvement of 7.8% in click-through rate (CTR) and 8.3% in user engagement compared to state-of-the-art models. This research presents a paradigm shift toward truly dynamic and explainable recommendation systems, creating a way for more personalized and user-centric experiences in the digital landscape.https://www.mdpi.com/2504-2289/9/5/124adaptive recommendation systemscross-domain personalizationknowledge graphs |
| spellingShingle | Zeinab Shahbazi Rezvan Jalali Zahra Shahbazi Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs Big Data and Cognitive Computing adaptive recommendation systems cross-domain personalization knowledge graphs |
| title | Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs |
| title_full | Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs |
| title_fullStr | Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs |
| title_full_unstemmed | Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs |
| title_short | Enhancing Recommendation Systems with Real-Time Adaptive Learning and Multi-Domain Knowledge Graphs |
| title_sort | enhancing recommendation systems with real time adaptive learning and multi domain knowledge graphs |
| topic | adaptive recommendation systems cross-domain personalization knowledge graphs |
| url | https://www.mdpi.com/2504-2289/9/5/124 |
| work_keys_str_mv | AT zeinabshahbazi enhancingrecommendationsystemswithrealtimeadaptivelearningandmultidomainknowledgegraphs AT rezvanjalali enhancingrecommendationsystemswithrealtimeadaptivelearningandmultidomainknowledgegraphs AT zahrashahbazi enhancingrecommendationsystemswithrealtimeadaptivelearningandmultidomainknowledgegraphs |