Ontology-based Recommendation Research Articles

Laptop Recommender System Using the Hybrid of Ontology-Based and Collaborative Filtering

In the era of ever-evolving information technology, choosing the best laptop can be a complicated task for many users. The increasing complexity of technical specifications is often an obstacle, especially for users who need help understanding them. In addressing this challenge, we propose a solution: a laptop recommendation system that considers users' preferences and functional needs. We designed this system to help users choose a laptop that suits their daily functional needs. This system uses a form of Conversational Recommender System (CRS) by combining Ontology-Based Recommender System Filtering and Collaborative Filtering (CF). Ontology-Based Recommender System Filtering ensures a strong relationship between functional needs and technical specifications of laptops, making it easier for users to identify the right laptop. At the same time, Collaborative Filtering (CF) can provide diversity to the recommended products by using similar user preference data. We evaluate the accuracy of our system by calculating the success rate of recommendation accuracy with the accuracy metric, and the evaluation results show that the success rate of recommendation accuracy reaches 93.33%. Our system is highly effective in assisting users in choosing a laptop that suits their functional needs. With our laptop recommendation system, users can confidently select the correct laptop without being burdened by technical specifications, thus making their lives easier and more productive.

Ontology-based recommender system: a deep learning approach

Ontology-based recommender system: a deep learning approach

Facilitating Students’ Adaptive Help-seeking and Peer Interactions through an Analytics-enhanced Forum in Engineering Design Education

Design often takes place in collective and collaborative settings, and interactions and mutual support among peers have been a critical component of design education. However, in most of the existing design courses, students often work in small groups and peer interactions are limited to group members, which limits the range and depth of knowledge exchange. To complement the group-based activities, this study designs and assesses an analytics-enhanced discussion forum for whole-class interactions. The forum adopts ontology-based recommender systems and anomaly detection techniques to tailor the threads and contents for individual students in a personalized way. This analytics-enhanced forum was implemented in a large-size undergraduate design course (n = 313), and data about student responses to this forum was compared with data from the previous year’s course that adopted a conventional forum (n = 280). From the statistical analysis, students learning with the analytics-enhanced forum demonstrated significantly higher degrees of design practices (specifically, empathize, define, ideate, and test), collaborative learning, and course satisfaction. Qualitative analysis of students’ focus-group interviews shows their perceived benefits and concerns of the analytics-enhanced forum. The study also suggests integrating generative artificial intelligence and large language models to support students’ design thinking and collaborative design.

Ontology-Based Recommender System for Personalized Physical Exercise in Obesity Management

In Indonesia, obesity is a serious health issue, and rates have risen recently because of sedentary lifestyles and poor eating practices. We suggest a proactive self-care suggestion system specifically created for Indonesians who are dealing with obesity to address this problem. Our recommender system attempts to give customers individualized suggestions for healthy lifestyle modifications that will make it easier for them to manage their weight. Because social media is so widely used in Indonesia, we created our system as a Telegram Chatbot. Our system may provide personalized suggestions based on a particular gender, activity level, fat mass, and difficulty of exercise that are relevant to Indonesians by fusing the user's ontological profile with generic clinical guidelines and standards for the management of obesity. Ontologies with Semantic Web Rule Language (SWRL) were used in the development of our system since SWRL ontologies are thought to perform better. Evaluations carried out using case studies and expert verification illustrate the usefulness of our suggested method, and the validated result of 88.8 percent demonstrates that our system can deliver good suggestion results for the user.

Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach

Feature selection plays an important role in machine learning and data mining problems. Identifying the best feature selection algorithm that helps to remove irrelevant and redundant features is a complex task. This research tries to address it by recommending a feature selection algorithm based on dataset meta-features. The main contribution of the work is the use of Semantic Web principles to develop a recommendation model for the feature selection algorithm. As a result, dataset meta-features are modeled in a domain ontology, and a set of Semantic Web rule language (SWRL) predictive rules have been proposed to recommend a feature selection algorithm. The result of this research is a feature selection algorithm recommendation based on the data characteristics and quality (FSDCQ) ontology, which not only helps with recommendations but also finds the data points with data quality violations. An experiment is conducted on the classification datasets from the UCI repository to evaluate the proposed ontology. The usefulness and effectiveness of the proposed method is evaluated by comparing it with the widely used method in the literature for the recommendation. Results show that the ontology-based recommendations are equally good as the widely used recommendation model, which is k-NN, with added benefits.

A Hybrid Ontology-based Recommender System Utilizing Data Enrichment and SVD Approaches

A Hybrid Ontology-based Recommender System Utilizing Data Enrichment and SVD Approaches

Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems.

Machine learning (ML) and especially deep learning (DL) with neural networks have demonstrated an amazing success in all sorts of AI problems, from computer vision to game playing, from natural language processing to speech and image recognition. In many ways, the approach of ML toward solving a class of problems is fundamentally different than the one followed in classical engineering, or with ontologies. While the latter rely on detailed domain knowledge and almost exhaustive search by means of static inference rules, ML adopts the view of collecting large datasets and processes this massive information through a generic learning algorithm that builds up tentative solutions. Combining the capabilities of ontology-based recommendation and ML-based techniques in a hybrid system is thus a natural and promising method to enhance semantic knowledge with statistical models. This merge could alleviate the burden of creating large, narrowly focused ontologies for complicated domains, by using probabilistic or generative models to enhance the predictions without attempting to provide a semantic support for them. In this paper, we present a novel hybrid recommendation system that blends a single architecture of classical knowledge-driven recommendations arising from a tailored ontology with recommendations generated by a data-driven approach, specifically with classifiers and a neural collaborative filtering. We show that bringing together these knowledge-driven and data-driven worlds provides some measurable improvement, enabling the transfer of semantic information to ML and, in the opposite direction, statistical knowledge to the ontology. Moreover, the novel proposed system enables the extraction of the reasoning recommendation results after updating the standard ontology with the new products and user behaviors, thus capturing the dynamic behavior of the environment of our interest.

A systematic review of ontology use in E-Learning recommender system

Ontology and knowledge-based systems typically provide e-learning recommender systems. However, ontology use in such systems is not well studied in systematic detail. Therefore, this research examines the development and evaluation of ontology-based recommender systems. The study also discusses technical ontology use and the recommendation process. We identified multidisciplinary ontology-based recommender systems in 28 journal articles. These systems combined ontology with artificial intelligence, computing technology, education, education psychology, and social sciences. Student models and learning objects remain the primary ontology use, followed by feedback, assessments, and context data. Currently, the most popular recommendation item is the learning object, but learning path, feedback, and learning device could be the future considerations. This recommendation process is reciprocal and can be initiated either by the system or students. Standard ontology languages are commonly used, but standards for student profiles and learning object metadata are rarely adopted. Moreover, ontology-based recommender systems seldom use the methodology of building ontologies and hardly use other ontology methodologies. Similarly, none of the primary studies described ontology evaluation methodologies, but the systems are evaluated by nonreal students, algorithmic performance tests, statistics, questionnaires, and qualitative observations. In conclusion, the findings support the implementation of ontology methodologies and the integration of ontology-based recommendations into existing learning technologies. The study also promotes the use of recommender systems in social science and humanities courses, non-higher education, and open learning environments.

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.

A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce

In Electronic commerce, customer reviews play a significant role in purchase making decision. Most of the existing recommendation systems consider the customer reviews, user purchase history and product rating for predicting the recommended product. Since the users interest are varying over time, the existing recommendation systems lack in finding the current relevant items to the customers. To overcome this problem, this article proposes a new fuzzy logic-based product recommendation system which dynamically predicts the most relevant products to the customers in online shopping according to the users’ current interests. A novel algorithm has been proposed in this paper for computing the sentimental score of the product with associated end user target category. Finally, the proposed fuzzy rules and ontology-based recommendation system uses ontology alignment for making decisions that are more accurate and predict dynamically based on the search context. The experimental results of the proposed recommendation system show better performance than the existing product recommendation systems in terms of prediction accuracy of the relevant products for target users and in the time taken to provide such recommendations.

Ontology-based Daily Menu Recommendation System for Complementary Food According to Nutritional Needs using Naïve Bayes and TOPSIS

Ontology-based Daily Menu Recommendation System for Complementary Food According to Nutritional Needs using Naïve Bayes and TOPSIS

Mapping of learning style with learning object metadata for addressing cold-start problem in e-learning recommender systems

In the e-learning domain, content recommender systems had evolved to recommend relevant learning contents based on the learner preferences. One of the significant drawbacks of content recommenders in the e-learning domain is the new user cold-start problem. The objective of this study is to propose a recommendation model for addressing the cold-start problem using learner's learning style alone. Learning style refers to the way a learner prefers to learn and it is a prominent learner characteristic to understand the learner profile. In this study, we propose an ontology-based recommendation algorithm that makes use of the learning dimensions of the Felder Silverman Learning Style Model to map with the learning object characteristics. The knowledge about the learner and the learning objects are represented using ontology. Experiments were conducted to evaluate the accuracy of the proposed recommendation model using the evaluation metric and f-measure. The learner satisfaction with the proposed model is measured based on the ratings given to the learning objects by the participants of the experiment.

A Hybrid Ontology-Based Recommendation System in e-Commerce

The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.

Ontology-based Recommendation System for Maintenance of Korean Architectural Heritage

Ontology-based Recommendation System for Maintenance of Korean Architectural Heritage

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

Review of ontology-based recommender systems in e-learning

In recent years there has been an enormous increase in learning resources available online through massive open online courses and learning management systems. In this context, personalized resource recommendation has become an even more significant challenge, thereby increasing research in that direction. Recommender systems use ontology, artificial intelligence, among other techniques to provide personalized recommendations. Ontology is a way to model learners and learning resources, among others, which helps to retrieve details. This, in turn, generates more relevant materials to learners. Ontologies have benefits of reusability, reasoning ability, and supports inference mechanisms, which helps to provide enhanced recommendations. The comprehensive survey in this paper gives an overview of the research in progress using ontology to achieve personalization in recommender systems in the e-learning domain.

Ontology-Based Food Recommendation System for Seniors: A Peer To Peer Networking Approach

Ontology-Based Food Recommendation System for Seniors: A Peer To Peer Networking Approach

Requirement specification of an ontology-based semantic recommender system for medical prescriptions and drug interaction detection

The prescription of proper drugs is very important for disease treatment and patient health. Search and analyse the resources to find the drug interactions is a time-consuming and difficult task. It is possible to accelerate this process and improve the quality of drug prescriptions using the life sciences information sources available on the semantic web. In this paper, requirement specification of a ontology-based semantic recommender system for medical prescriptions and drug interaction detection has been presented. The main functions of the system and its requirements were initially extracted and described in detail by observing the existing system in the hospitals, interviewing medical specialists, and analysing questionnaires. Then, an appropriate ontology was designed and implemented for the system by using Protege to detect drug interactions. A prototype was developed using Java to evaluate the functions of drug recommendations and drug interaction detection. The system performance was evaluated under different scenarios compared with similar approaches for drug prescription and interaction detection. The evaluation results demonstrated the enhanced performance of the proposed approach in both drug prescription and interaction detection functions. This indicates the benefits of proposed system as a computerised physician ordering entry.

Requirement specification of an ontology-based semantic recommender system for medical prescriptions and drug interaction detection

Requirement specification of an ontology-based semantic recommender system for medical prescriptions and drug interaction detection

Inferring preferences in ontology-based recommender systems using WOWA

In content-based semantic recommender systems the items to be considered are defined in terms of a set of semantic attributes, which may take as values the concepts of a domain ontology. The aim of these systems is to suggest to the user the items that fit better with his/her preferences, stored in the user profile. When large ontologies are considered it is unrealistic to expect to have complete information about the user preference on each concept. In this work, we explain how the Weighted Ordered Weighted Averaging operator may be used to deduce the user preferences on all concepts, given the structure of the ontology and some partial preferential information. The parameters of the WOWA operator enable to establish the desired aggregation policy, which ranges from a full conjunction to a full disjunction. Different aggregation policies have been analyzed in a case study involving the recommendation of touristic activities in the city of Tarragona. Several profiles have been compared and the results indicate that different aggregation policies should be used depending on the type of user. The amount of information available in the ontology must be also taken into account in order to establish the parameters of the proposed algorithm.