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    Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey
    (Pergamon-Elsevier Science Ltd, 2022) Khan, Nasrullah; Ma, Zongmin; Ullah, Aman; Polat, Kemal
    Recommender Systems (RS) are established to deal with the preferences of users to enhance their experience and interest in innumerable online applications by streamlining the stress persuaded by the reception of excessive information through the recommendation methods. Although researches have put a lot of efforts in making recommendation processes accurate, specific, and personalized; different issues like cold start, data sparsity or gray sheep etc., still pop up in one or the other form of challenges. Recently, exploitation of Knowledge Graph (KG)-based data as Side Information in recommendation methods has revealed as a sign of resolution to the corresponding challenges; and thus, acquired incredible focus, applicability, and popularity. The incorporation of KG in recommendation has not only effectively alleviated the contrasting challenges, but also has provided specific, accurate, personalized and explainable recommendations about the target items to the end users. In this paper, we explore well-known RSs, popular knowledge repositories, benchmark datasets, recommendation methods, and future research dimensions about the current research. Intuitively, we investigate recommendation methods and associated datasets with respect to the corresponding application scenarios in a categorical way.
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    Continual knowledge graph embedding enhancement for joint interaction-based next click recommendation
    (Elsevier B.V., 2024) Khan, Nasrullah; Ma, Zongmin; Ma, Ruizhe; Polat, Kemal
    Knowledge Graph Embedding (KGE) based deep neural networks contribute to recommender systems in diverse application scenarios. However, Catastrophic Forgetting (CForg) significantly degrades their performance. Although exemplar replay is commonly adopted as a possible remedy to alleviate the intensity of CForg, a trade-off between performance and complexity occurs in this process. Therefore, in this work, we introduce Continual Knowledge graph embedding enhancement for joint Interaction-based Next click recommendation (CKIN) to defy the CForg and assuage the complexity. Typically, we introduce the Semantic Relevance Estimation (SRE) technique to ensure information relevance by filtering out irrelevant-data and reducing the space complexity. We introduce the SRE-enhanced deep probabilistic technique to probably replay the most relevant exemplars to defy the CForg and reduce the time complexity. Moreover, we introduce the integration of locality-preserving loss into the KGE framework to optimize the loss. In substantial experiments on real-world datasets, CKIN outperforms the baseline methods by effectively meeting the highlighted challenges. © 2024 Elsevier B.V.
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    DCA-IoMT: Knowledge-graph-embedding-enhanced deep collaborative alert recommendation against COVID-19
    (IEEE-Institute Electrical Electronics Engineers Inc, 2022) Khan, Nasrullah; Ma, Zongmin; Ullah, Aman; Polat, Kemal
    Filtration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular features and tendencies of the context. Additionally, the integration of the location-based current data of coronavirus disease 2019 (COVID-19) into the KG is necessary for the recommendation of region-aware precautionary alerts to the concerned people-an essential application of the current and future Internet of Medical Things. Therefore, in this article, we propose a novel deep collaborative alert recommendation (DCA) approach to cope with the situation. Particularly, DCA collects current online data about COVID-19, purifies, and transforms them to the KG. Furthermore, it independently encapsulates the cocurricular features and tendencies of the context in the embedding space and encodes them to the independent hidden factors via a graph neural network. The bi-end hidden factors are computed via matrix factorization to infer the potential connections. Moreover, a relevance estimator and a cross transistor are configured to enhance the generalization capability of the model. Experiments on two real-world datasets are performed to evaluate the effectiveness of DCA. Results and analysis show that the proposed approach has outperformed the baseline methods with fine improvements in providing the required recommendations.
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    Similarity attributed knowledge graph embedding enhancement for item recommendation
    (Elsevier Science Inc, 2022) Khan, Nasrullah; Ma, Zongmin; Ullah, Aman; Polat, Kemal
    Knowledge Graph Embedding (KGE)-enhanced recommender systems are effective in providing accurate and personalized recommendations in diverse application scenarios. However, such techniques that exploit entire embedded Knowledge Graph (KG) without data relevance approval constraints fail to stop noise penetration into the data. Additionally, approaches that pay no heed to tackle semantic relations among entities remain unable to effectively capture semantical structure of Heterogeneous Information Graph (HIG). Therefore, in this paper, we propose Similarity Attributed Graphembedding Enhancement (SAGE) approach to model similarity-aware semantic connections among entities according to their triplets' granularity. SAGE is a novel Knowledge Graph Embedding Enhancement (KGEE) method that constructs Entity-relevance-based Similarity-attributed Subgraph (ESS) to remove noise from the underlying data. It propagates interactions-enhanced knowledge over ESS to learn higher-order semantic connections among entities; and simultaneously utilizes feedbacks to enhance the interactions and regularize the model to highlight influential targets (nodes). Further, it samples influential targets in KG, independently move their preferences to the Local Central Nodes (LCN) of current influential areas, and streamline the collected information from all LCN to the main unit. Finally, a prediction module is used to determine generalized preferences for recommendation. We performed extensive experiments on benchmark datasets to evaluate the performance of SAGE where it outperformed the state-of-the-art methods with significant improvements in effectively providing the desired explainable recommendations.