Knowledge-based Recommender System Research Articles

Personalised Course Recommender: Linking Learning Objectives and Career Goals through Competencies

This paper presents a Knowledge-Based Recommender System (KBRS) that aims to align course recommendations with students' career goals in the field of information systems. The developed KBRS uses the European Skills, Competences, qualifications, and Occupations (ESCO) ontology, course descriptions, and a Large Language Model (LLM) such as ChatGPT 3.5 to bridge course content with the skills required for specific careers in information systems. In this context, no reference is made to the previous behavior of students. The system links course content to the skills required for different careers, adapts to students' changing interests, and provides clear reasoning for the courses proposed. An LLM is used to extract learning objectives from course descriptions and to map the promoted competency. The system evaluates the degree of relevance of courses based on the number of job-related skills supported by the learning objectives. This recommendation is supported by information that facilitates decision-making. The paper describes the system's development, methodology and evaluation and highlights its flexibility, user orientation and adaptability. It also discusses the challenges that arose during the development and evaluation of the system.

Knowledge Based Recommender System for Disease Diagnostic and Treatment Using Adaptive Fuzzy-Blocks

Knowledge Based Recommender System for Disease Diagnostic and Treatment Using Adaptive Fuzzy-Blocks

Knowledge-based recommender systems: overview and research directions.

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

Can ChatGPT provide appropriate meal plans for NCD patients?

ObjectivesDietary habits significantly affect health conditions and are closely related to the onset and progression of non-communicable diseases (NCDs). Consequently, a well-balanced diet plays an important role in lessening the effects of various disorders, including NCDs. Several artificial intelligence recommendation systems have been developed to propose healthy and nutritious diets. Most of these systems use expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. However, new advances in large language models such as ChatGPT, with their ability to produce human-like responses, have led individuals to search for advice in several tasks, including diet recommendations. This study aimed to determine the ability of ChatGPT models to generate appropriate personalized meal plans for patients with obesity, cardiovascular diseases, and type 2 diabetes. MethodsUsing a state-of-the-art knowledge-based recommendation system as a reference, we assessed the meal plans generated by two large language models in terms of energy intake, nutrient accuracy, and meal variability. ResultsExperimental results with different user profiles revealed the potential of ChatGPT models to provide personalized nutritional advice. ConclusionAdditional supervision and guidance by nutrition experts or knowledge-based systems are required to ensure meal appropriateness for users with NCDs.

A Survey on Recommendation System Techniques

The primary objective of recommender systems (RS) is to analyze user behavior and propose relevant items or services that users would find appealing. Recommender systems have gained significant prominence in various domains such as information technology and e-commerce. They achieve this by customizing recommendations based on individual preferences, efficiently filtering options from a vast pool, and enabling users to discover content that matches their interests. Numerous recommendation techniques have been developed to generate personalized suggestions, including collaborative filtering, content-based filtering, knowledge-based recommendation systems, and other approaches. Furthermore, hybrid recommendation systems have been proposed to address the limitations of individual methods by combining different techniques. This paper presents an overview of diverse recommendation methods, their fundamental approaches, challenges, solution and have equally looked at different solutions to these challenges faced by modern recommender systems. It also recommends promising avenues for future directions.

Knowledge based topic retrieval for recommendations and tourism promotions

The Users regularly share their views on services through internet reviews. Digital tourism allows travelers to plan and manage their trips ahead of time using the internet. The proposed system will enable users to create recommender systems that can help to find new places as per individual interests. The portals such as Tripadvisor and Yelp assist travelers in decision-making and providing feedback. The data available in the form of reviews, blogs, and rating help the researcher build a better recommendation and promotion system. The recommendation system will help tourists discover restaurants often ignored due to a lack of promotion. This research proposes an automated system to perform three subtasks: predicting star ratings from reviews, a feedback model, and a knowledge-based recommender system. The experimental work results show that Random Forest and Decision Tree classifiers give the highest accuracy for predicting star ratings. The research utilizes clustering and topic modeling to identify the topic from the reviews. Moreover, this research uses salience and valence of the topic to generate the feedback model. Furthermore, a recommender system is developed using user preferences, restaurant knowledge, and the valence of each topic of each restaurant. Inverse Document Frequency and K-means clustering have been used to improve the recommendation quality and construct a CSR matrix. This system will link restaurants with similar reviews to ensure that customers find restaurants almost identical to the ones they enjoyed in the past.

High-Performance Actionable Knowledge Miner for Boosting Business Revenue

This research proposes a novel strategy for constructing a knowledge-based recommender system (RS) based on both structured data and unstructured text data. We present its application to improve the services of heavy equipment repair companies to better adjust to their customers’ needs. The ultimate outcome of this work is a visualized web-based interactive recommendation dashboard that shows options that are predicted to improve the customer loyalty metric, known as Net Promoter Score (NPS). We also present a number of techniques aiming to improve the performance of action rule mining by allowing to have convenient periodic updates of the system’s knowledge base. We describe the preprocessing-based and distributed-processing-based method and present the results of testing them for performance within the RS framework. The proposed modifications for the actionable knowledge miner were implemented and compared with the original method in terms of the mining results/times and generated recommendations. Preprocessing-based methods decreased mining by 10–20×, while distributed mining implementation decreased mining timesby 300–400×, with negligible knowledge loss. The article concludes with the future directions for the scalability of the NPS recommender system and remaining challenges in its big data processing.

Customized Curriculum and Learning Approach Recommendation Techniques in Application of Virtual Reality in Medical Education

Virtual Reality (VR) has made considerable gains in the consumer and professional markets. As VR has progressed as a technology, its overall usefulness for educational purposes has grown. On the other hand, the educational field struggles to keep up with the latest innovations, changing affordances, and pedagogical applications due to the rapid evolution of technology. Therefore, many have elaborated on the potential of virtual reality (VR) in learning. This research proposes a novel techniques customized curriculum for medical students and recommendations for their learning process based on deep learning techniques. Here the data has been collected based on the pre-historic performance of the student and their current requirement and these data have been created as a dataset. Then this has been processed for analysis based on CAD system integrated with deep learning techniques for creating a customized curriculum. Initially this data has been processed and analysed to remove missing and invalid data. Then these data were classified for creation of the curriculum using a gradient decision tree integrated with naïve Bayes. From this, the customized curriculum has been generated. Based on this customized curriculum, the learning approach recommendation has been carried out using the fuzzy rules integrated knowledge-based recommendation system. The experimental results of the proposed technique have been carried out with an accuracy of 98%, specificity of 82%, F-1 score of 79%, information overload of 75%, and precision of 81%.

A case-based reasoning system to recommend solutions for source water protection: knowledge acquisition and modelling

ABSTRACT “How can drinking water sources be protected, and how can one learn from others who have faced similar problems?” These questions are becoming increasingly relevant in a context where anthropogenic activities threaten natural resources. We describe in this paper a knowledge acquisition and modelling process to support complex decision-making related to source water protection. We conducted online surveys, interviews and applied thematic analysis to gather and structure knowledge from documented and undocumented sources. Our process resulted in structured descriptions of cases that consist of past problems and their implemented solutions. Case design was based on know-how and knowledge needs expressed by water stakeholders. The case base forms the backbone of a knowledge-based recommender system prototype that implements case-based reasoning. It is meant to guide decision-makers in developing solutions based on past experiences. It is a successful application of knowledge management and sharing of lessons learned for decision-making in practice.

Movie Recommendation System Using Knowledge-Based Filtering and K-Means Clustering

The movie recommender system has an important role in providing movie recommendations for users, but new users have difficulty choosing movies that are given by the recommender system because of the cold start problem. This study aims to overcome the cold start problem using a knowledge-based recommender system, i.e association rule mining using an apriori algorithm. The apriori algorithm aims to extract correlations between product itemsets, but the problem in the apriori algorithm is the large number of association rules that make the complex computation. To overcome this problem, we combine the apriori algorithm and k-means to produce more accurate recommendations, because the items are grouped before the recommendation process using the k-means algorithm. In this study, we use a dataset of movies and ratings from the Kaggle website. This study uses a minimum value of 0.5 confidence, and a minimum value of 4 lifts. To produce the best itemset in the form of antecedents and consequents of the Beauty and the Beast item with The Passion of Joan of Arc which has a value of 0.107981 support, 0.779661 confidence, 4.151695 lift

Knowledge-Based Recommender System Using Artificial Intelligence for Smart Education

Artificial intelligence can open modern opportunities and potentials for smart education. Smart learning purposes at providing holistic learning to learners utilizing modern technologies to fully prepare them for a fast-evolving world where adaptability is vital. With the advancement of technologies and within modern society, smart education will pose several challenges, like educational structures, pedagogical theory, educational ideology, technology leadership, and teachers’ learning leadership. Therefore, in this paper, an Intelligent Knowledge-based recommender system (IKRS) has been proposed using artificial intelligence for smart education. The recommendation is generated by the genetic algorithm and K-nearest neighbor algorithm (KNN) utilizing the optimized weight attributes vectors that signify the learner’s opinions. The experimental results show that the suggested IKRS model enhances student-teacher interaction, student involvement level, learning quality and predicts students’ learning style compared to other existing methods.

Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs

Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs

An Effective Knowledge-Based Recommendation System for Supply Chain Information Based on UserCF Algorithm

An Effective Knowledge-Based Recommendation System for Supply Chain Information Based on UserCF Algorithm

A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization

Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.

Ontology-Based Recommender System for a Learning Sequence in Programming Languages

The same learning process in educational systems could be boring and time consuming for some learners. This problem arises from the lack of personalized learning sequence for learners with different knowledge level. Recommender systems play an important role in assisting the learners to find suitable learning materials and personalized learning sequence. Use of ontology for knowledge representation in knowledge-based recommender systems would facilitate sharing, reuse and common terminology. Since programming concepts have logical relationships among together so, traditional education systems are more stressful and very time-consuming. This paper aims to propose an ontology based recommender system to present a Personalized Learning Sequence in Programming (PLSP) domain which is depended to learner's knowledge level. A recommender module and, the knowledge base module are integrated together in the proposed framework. The recommender module as the main module in the framework, has three stages which is working based on semantic rules and ontology representation. Evaluation of the system was carried out by comparing the non-recommender system (web-based search) using 32 ICT respondents. Results demonstrate that the participants who used the proposed system spent 1119 seconds to find the suitable learning path in comparison to those who used a non-recommender system (3480 seconds) in the same learning material. It means that learners who follow learning path with PLSP, are more suitable for them. Furthermore, the average mean value of usability test is 4.47, (5 maximum scale) which indicates that the system proved to be useful, was easy to use, and satisfied the users.

NLP-Based Customer Loyalty Improvement Recommender System (CLIRS2)

Structured data on customer feedback is becoming more costly and timely to collect and organize. On the other hand, unstructured opinionated data, e.g., in the form of free-text comments, is proliferating and available on public websites, such as social media websites, blogs, forums, and websites that provide recommendations. This research proposes a novel method to develop a knowledge-based recommender system from unstructured (text) data. The method is based on applying an opinion mining algorithm, extracting aspect-based sentiment score per text item, and transforming text into a structured form. An action rule mining algorithm is applied to the data table constructed from sentiment mining. The proposed application of the method is the problem of improving customer satisfaction ratings. The results obtained from the dataset of customer comments related to the repair services were evaluated with accuracy and coverage. Further, the results were incorporated into the framework of a web-based user-friendly recommender system to advise the business on how to maximally increase their profits by introducing minimal sets of changes in their service. Experiments and evaluation results from comparing the structured data-based version of the system CLIRS (Customer Loyalty Improvement Recommender System) with the unstructured data-based version of the system (CLIRS2) are provided.

Recommender System: A bibliometric analysis

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.

Logical foundations of knowledge-based recommender systems: A unifying spectrum of alternatives

The aim of this paper is to provide logical foundations for knowledge-based recommender systems, for which, unlike other problem solving tasks, a comprehensive formalization is not yet available. This goal is justified by the need to compare recommenders based on the way they use knowledge to generate recommendations and, consequently, on the underlying semantics of the recommendation process itself. Moreover, since the here adopted logical formalization has been borrowed from other tasks such as diagnosis, many interesting results and opportunities can be transposed from such tasks to recommendation.While we do not aim at proposing a new recommendation generation technique, we believe that our formalization will be the basis for unifying different approaches to knowledge-based recommendation, revealing their semantics and offering a conceptual framework to compare them. In fact, the framework covers different variations of knowledge-based recommendation, such as context-aware, constraints-based, package and group recommendation, as well as recommendation based on negative preferences.

An interactive knowledge-based recommender system for fashion product design in the big data environment

In this paper, we originally propose an interactive, knowledge-based design recommender system (IKDRS) for relevant personalised fashion product design schemes with their virtual demonstrations for a specific consumer. This system enables the iterative interaction between virtual product demonstration and the designer’s professional knowledge and perception in order to find the best existing design solution, i.e. combination of basic garment elements. To develop this system, the anthropometric data and designer’s perception of body shapes are first acquired by using a 3D body scanning system and a sensory evaluation procedure. Next, an instrumental experiment is realised for measuring the technical parameters of fabrics and five sensory experiments are carried out in order to acquire design knowledge. The acquired data are used to classify body shapes and model the relations between human bodies, fashion themes and design factors by using fuzzy techniques. From these models, we set up an ontology-based design knowledge base, including key data and relevant relation models. This knowledge base can be updated in a big data environment by progressively learning from new design cases. On this basis, we propose an interactive, personalised design recommender system. This system works through a newly proposed design process: consumers’ emotional requirement identification – design schemes generation – recommender – 3D virtual prototype display and evaluation – design factors adjustment. This process can be performed repeatedly until the designer is satisfied. The proposed system has been validated through a number of successful real design cases.

Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System.

BackgroundHealth education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds.ObjectiveThe aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system.MethodsA knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP).ResultsThe constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628.ConclusionsThis study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.