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Internet of Things (IoT) is an advancing technology that is a network of many smart devices connected together to provide services to various application domains such as smart offices, health monitoring, agriculture, etc. IoT-based recommendation technologies are becoming one of the key requirements that will recommend future IoT solutions. A review of existing recommendation technologies in the vibrant field of IoT is discussed in this paper. The main aim of this paper is to present a comprehensive analysis of existing literature on recommendation approaches. Several issues of applying recommendation systems to IoT are also discussed. Nearly 1000 research papers have been considered for analyses which are published by ACM, Springer, IEEE and Science Direct from 2011 to 2017. Finally, the recent research trends are spotted for future researchers intended to work in the recommendation-based IoT domain. Moreover, this paper also envisages the future of the Recommender System (RS) that opens up the newest research directions for young researchers.

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively.

With web-based education and Technology Enhanced Learning (TEL) assuming new importance, there has been a shift towards Massive Open Online Courses (MOOC) platforms owing to their openness and flexible \"on-the-go\" nature. The previous decade has seen tremendous research in the field of Adaptive E-Learning Systems but work in the field of personalization in MOOCs is still a promising avenue. This paper aims to discuss the scope of said personalization in a MOOC environment along with proposing an approach to build a knowledge-based recommendation system that uses multiple domain ontologies and operates on semantically related usage data. The recommendation system employs cluster-based collaborative filtering in conjunction with rules written in the Semantic Web Rule Language (SWRL) and thus is truly a hybrid recommendation system. It has at its core, clusters of learners which are segregated using predicted learning style in accordance with the Felder Silverman Learning Style Model (FSLSM) through the detection of tracked usage parameters. Recommendations are made to the granularity of internal course elements along with learning path recommendation and provided general learning tips and suggestions. The study is concluded with an observed positive trend in the learning experience of participants, gauged through click-through log and explicit feedback forms. In addition, the impact of recommendation is statistically analyzed and used to improve the recommendations.

Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sports. These systems support people in sports, for example, by the recommendation of healthy and performance-boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss future research directions.

This study presents a novel methodology for mapping Fault- and Thrust-based Structural Lineaments (FT-SL) in the rugged and inaccessible Oued-Laou watershed of the Rif Belt, Morocco. Combining optical (Landsat-8 OLI, Sentinel-2 MSI) and radar (Sentinel-1 SAR) remote sensing data, the research employs manual, semi-automatic, and automatic extraction methods enhanced by spatial filtering (Sobel, Laplacian, Kuan). A Knowledge-Based System (KBS) integrated with Multi-Criteria Decision Analysis (MCDA) evaluates the effectiveness of these methods, focusing on lineament statistics, orientation, density distribution, and correlation with existing geological maps. The results highlight Sentinel-1 SAR’s superior performance in detecting subsurface structures, while manual extraction yields the highest accuracy. This study also demonstrates the potential for generalizing this approach to other Alpine orogenic regions, such as the Alps, due to shared geological characteristics. The findings provide a robust framework for structural lineament mapping in mountainous terrains, addressing challenges of accessibility and data scarcity.

The exponential growth in the online share of businesses has to lead to a gigantic wave of options available to the active user. Recommender systems, therefore assist the users to go through the tailored list of products to match their preferences. A range of recommender systems is available to serve the purpose. This article will navigate through the basic of recommender systems, and its classifications types viz. collaborative filtering, content-based filtering, demographic, hybrid, and knowledge-based recommender system. It aims to analyze publications of the Scopus database using biblioshiny tool of RStudio software. A bibliometric analysis is conducted on 556 papers to analyze the recent research trends in recommendation systems. Further, challenges have also been discussed that need to be dealt with the recommender system.

Background Drug repositioning is the process of identifying new targets for known drugs. It can be used to overcome problems associated with traditional drug discovery by adapting existing drugs to treat new discovered diseases. Thus, it may reduce associated risk, cost and time required to identify and verify new drugs. Nowadays, drug repositioning has received more attention from industry and academia. To tackle this problem, researchers have applied many different computational methods and have used various features of drugs and diseases. Results In this study, we contribute to the ongoing research efforts by combining multiple features, namely chemical structures, protein interactions and side-effects to predict new indications of target drugs. To achieve our target, we realize drug repositioning as a recommendation process and this leads to a new perspective in tackling the problem. The utilized recommendation method is based on Pareto dominance and collaborative filtering. It can also integrate multiple data-sources and multiple features. For the computation part, we applied several settings and we compared their performance. Evaluation results show that the proposed method can achieve more concentrated predictions with high precision, where nearly half of the predictions are true. Conclusions Compared to other state of the art methods described in the literature, the proposed method is better at making right predictions by having higher precision. The reported results demonstrate the applicability and effectiveness of recommendation methods for drug repositioning.

The rapid expansion of digital platforms and the increasing complexity of user preferences have driven the need for more sophisticated recommendation systems. While Collaborative Filtering and Knowledge-Based Filtering have been widely adopted as core techniques for personalized recommendations, their individual limitations have led to the rise of hybrid approaches. Despite significant advancements, a comprehensive understanding of hybridization methodologies, their technical implementations, and emerging challenges remains unsolved. The purpose of this research is to systematically examine and synthe-size the domain of Hybrid Recommender Systems to address this. This study presents a scoping review, following the PRISMA-ScR guidelines, to systematically examine the domain of hybridizing Collaborative Filtering and Knowledge-Based Filtering. A total of 62 hybrid recommenders across various application domains were analyzed, and categorized into three primary hybridization strategies: Model Fusion, Transfer Learning, and Hierarchical Models. The review explores technical characteristics, hybridization techniques, data sources, evaluation methodologies, and domain-specific applications. Key findings indicate that most hybrid approaches focus on leveraging graph-based models, deep learning architectures, and causal inference techniques to enhance recommendation outcomes. However, despite these advancements, critical gaps remain. The review identifies key challenges, including computational complexity, lack of explainability, bias in recommendations, and reliance on offline evaluation metrics. Additionally, scalability issues in knowledge graph maintenance and the need for user-centered evaluation frameworks highlight important directions for future research. Addressing these gaps will be crucial in making hybrid recommendation systems more efficient, interpretable, and adaptable across diverse domains. This study contributes to the field by providing a structured synthesis of existing hybridization techniques, pinpointing success factors, and proposing future research avenues to advance hybrid recommendation systems.

In recent years, the research of combining a knowledge graph with recommendation systems has caused widespread concern. By studying the interconnections in knowledge graphs, potential connections between users and items can be discovered, which provides abundant and complementary information for recommendation of items. However, most existing studies have not effectively established the relation between entities and users. Therefore, the recommendation results may be affected by some unrelated entities. In this paper, we propose a neighborhood aggregation collaborative filtering (NACF) based on knowledge graph. It uses the knowledge graph to spread and extract the user’s potential interest, and iteratively injects them into the user features with attentional deviation. We conducted a large number of experiments on three public datasets; we verifyied that NACF is ahead of the most advanced models in top-k recommendation and click-through rate (CTR) prediction.

Background In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics. Results Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives. We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases. Conclusions We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided.