Rule-based Recommendation Research Articles

A Rule-Based System for Admission Recommendations in Nigerian Tertiary Institutions

This research presents a rule-based system to provide admission recommendations in Nigerian tertiary institutions. Unlike many existing studies that employ machine learning classifiers, this research aims to develop a transparent and deterministic system using predefined rules. The rule-based system recommends suitable courses based on candidates’ O’level results, Unified Tertiary Matriculation Examination (UTME) scores, computed post-UTME scores, and specific eligibility criteria. By focusing on a rule-based approach, the system mitigates the limitations and potential misclassifications associated with machine learning, thereby enhancing the efficiency and accuracy of the admissions process. The methodology follows predefined guidelines to ensure a transparent and understandable admissions procedure. This approach avoids the complexities and uncertainties of predictive models, providing an accurate and reliable method for course recommendations. The rule-based recommendation system ultimately aims to support educational institutions and admission offices in making informed decisions regarding candidate qualifications, eligibility, and course recommendations.

Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText classification method

To comprehend the genuine reading habits and preferences of diverse user cohorts and furnish tailored reading recommendations, this study introduces an English text reading recommendation model designed specifically for long-tail users. This model integrates collaborative filtering algorithms with the FastText classification method. Initially, the integrated collaborative filtering algorithm is explicated, followed by the calculation of the user's interest distribution across various types of English texts, achieved through an enhanced Ebbinghaus forgetting curve and analysis of user reading behaviors. Subsequently, an intelligent English text reading recommendation is generated by amalgamating collaborative filtering algorithms with association rule-based recommendation algorithms. Through optimization of the recommendation generation process, the model's recommendation accuracy is enhanced, thereby augmenting the performance and user satisfaction of the recommendation system. Finally, a comparative analysis is conducted with respect to the Top-N algorithm model, matrix factorization-based algorithm model, and FastText classification model, illustrating the superior recommendation accuracy and F-Measure value of the proposed model. The study findings indicate that when the recommendation list contains 10, 30, 50, and 70 texts, the recommendation accuracy of the proposed algorithm model is 0.75, 0.79, 0.8, and 0.74, respectively, outperforming other algorithms. Furthermore, as the number of texts increases, the F-Measure of all four models gradually improves, with the final F-Measure of the proposed model reaching 0.81. Notably, the F-Measure of the English text reading recommendation model proposed in this study significantly surpasses that of the other three recommendation methods. Demonstrating commendable performance in recall rate, root mean square error, normalized cumulative gain, precision, and accuracy, the model adeptly reflects user reading interests, thereby enhancing the accuracy of text recommendations and the overall system performance. The study findings offer crucial insights and guidance for enhancing the accuracy and overall efficacy of English text recommendation systems.

Research on the Recommendation System of Music e-learning Resources with Blockchain based on Hybrid Deep Learning Model

Learners are confronted with an ever-growing array of diverse and complex educational resources as music education increasingly moves to online platforms. Traditional resource curation methods, which rely heavily on educators, fall short of meeting the dynamic needs of modern students. To address this issue, we present a novel recommendation system for music e-learning resources that combines the power of blockchain technology with a hybrid deep learning model. Our model combines blockchain's robust security and transparency features with advanced deep learning algorithms, enhancing the personalization and efficiency of resource recommendations. A backpropagation neural network with K nearest neighbor classification, traditional collaborative filtering (CF), and an improved CF algorithm are used in the hybrid approach. For the back propagation neural network algorithm, K nearest neighbor classification algorithm, traditional collaborative filtering (CF) and improved CF algorithm, the accuracy rate of improved CF algorithm is higher, reaching 95%. Comparing the proposed model with the association rule-based recommendation model and the content-based recommendation model, the model constructed in this study received high evaluation from experts, with an average score of 98, and more than 97% of them gave a high score of 95 or more, and the evaluation of experts tended to be consistent. Overall, the model proposed in this study can make better recommendations for music education learning resources and bring users a good learning experience, so this study has some practical application value. This research demonstrates a highly effective, blockchain-enhanced recommendation system for music e-learning resources. Our model has significant practical value and potential for adoption in online music education platforms because it provides tailored educational content and an enhanced learning experience.

Download Behavior Analysis Based on Google Play Store Data

As mobile apps continue to grow, app stores, as the main channel for users to download apps, are becoming increasingly important for developers and platforms. Understanding users 'download behavior and accurately predicting users' preferences and needs can effectively improve the effect of the recommendation system in the application mall, improve user experience and improve the download conversion rate. However, traditional rule-based recommender systems often face the problems of data sparsity and model complexity. Therefore, it is an urgent and valuable topic to analyze user download behavior combined with machine learning technology and to provide personalized recommendations and services. This study uses the download behavior information of Google Play Store users in 2018, and use three classic machine algorithms (linear regression, random forest and SVM) to model and predict the software rating, dig deep into various factors affecting the rating, and gain deep insight into users' preferences and behavior patterns. This will provide more accurate recommendation results for the application mall, improve the application quality and popularity, and improve the user satisfaction and loyalty, and provide an important reference for optimizing the recommendation system and personalized service of the application mall.

An Intelligent Manufacturing Models Recommendation Model Based on Knowledge Graph and Recommendation Algorithms

With the concept of intelligent manufacturing, a variety of intelligent manufacturing models emerge. But companies don’t have enough knowledge to choose the suitable intelligent manufacturing model. To solve this problem, based on lean design principles, combined with expert knowledge and expertise, we propose an Intelligent Manufacturing Models Recommendation Model (referred to as IMMR Model). First, we categorize customer requirements into real need, customer preference and customer expectation to accurately identify customer requirements. Then, we use knowledge graph to build a professional knowledge base for intelligent manufacturing, and form customized intelligent manufacturing models based on lean design. Finally, a recommendation system is constructed by combining an ontology rule-based recommendation algorithm and a similarity-based recommendation algorithm to recommend suitable intelligent manufacturing models for different customer requirements.

Модель и алгоритм поддержки принятия решения по выбору продуктов для рекомендации пользователю на основе метода анализа статистической импликации

The article considers the issues of analyzing data that the consumer encounters when choosing products and services. The problem is extracting useful information that allows offering the user new products and services depending on his preferences. This problem is localized by recommender systems focused on using the data mining methods, such as classification, clustering, analysis of association rules - a machine learning method that detects relationships between variables in databases. Compared to other methods, the advantage of the association rule-based recommender method is its transparency: the method can show the user the inference mechanism used to make decisions. Association rule-based recommender systems use two measures that are widely popular and evaluate sets of elements and create sets of association rules, the support measure and the confidence measure. However, to get better recommendations, the quality of association rules and the way sentence ranking should be measured by some objective measure. There has been developed a model and a decision support algorithm for the choice of products for recommendations to the user based on the statistical implication analysis method. In the proposed solutions, support and credibility measures are used to create association rules; a measure of the intensity of the statistical implication is used to filter the set of rules and to rank the recommendations.

A Data Mining Approach for Developing Online Streaming Recommendations

ABSTRACT Online streaming has become increasingly popular with the availability of broadband networks and the increase in computing power and electronic distribution. Online streaming operators have difficulty developing flexible business alternatives according to users’ changing streaming behaviors in terms of generating a good and profitable business model. In terms of e-commerce development, the live-streaming platform that provides streaming of the main merchandise to users, allowing the users to directly consume via live-streaming become critical issues. In this regard, personalized recommendation systems can use the user’s interests and purchasing behavior to recommend information and merchandise. Thus, this study investigates the online streaming experiences of Taiwanese consumers to evaluate online streaming users and their online purchase behaviors for developing online recommendations. This study uses a snowflake schema, which is an extension of the star schema. In addition, this study develops a rule-based recommendation approach for investigating online streaming and purchasing behaviors in terms of online recommendations. This study is the first to determine how online streaming proprietors and their affiliates are disseminated using online streaming consumer behaviors in terms of online recommendations for further electronic commerce development.

Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial.

BackgroundContinuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory.ObjectiveThe aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance.MethodsThis is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea–Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient’s CPAP compliance from the very first days of usage; (2) machine learning–based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient’s compliance, satisfaction, and health care costs.ResultsRandomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13% (8/60) were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients’ satisfaction was excellent in both arms, and up to 88% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean €90.2 (SD 53.14) (US $105.76 [SD 62.31]); intervention: mean €96.2 (SD 62.13) (US $112.70 [SD 72.85]); P=.70; €1=US $1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis.ConclusionsA machine learning–based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients’ empowerment in the management of chronic diseases.Trial RegistrationClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958

Investigating sports behaviors and market in Taiwan for sports leisure and entertainment marketing online recommendations

Sports are not only physical and mental activities, but also include leisure and entertainment activities. Sports leisure and entertainment marketing is an element of sports promotion that involves a wide variety of sectors and events in the sports industry. This particular style of marketing is concerned less with using a single strategy and more with using the content of sports, leisure and entertainment to assist online marketing efforts. This study examines the behaviors of Taiwan sports behaviors and market, a total of 1200 valid questionnaire data is organized for a database design. Data mining approach for entertainment computing, including cluster analysis and association rules, is used to determine the sports models of delivery and products. Using cluster analysis, we divide Taiwan sports participants into three clusters and then find each group’s profiles. In addition, this study implements a rule-based recommendation approach, association rules, for investigating subjects’ sporting as well as leisure and entertainment behaviors in terms of sports leisure and entertainment marketing and personalized recommendations.

Two-Sided Fairness in Non-Personalised Recommendations (Student Abstract)

Recommender systems are one of the most widely used services on several online platforms to suggest potential items to the end-users. These services often use different machine learning techniques for which fairness is a concerning factor, especially when the downstream services have the ability to cause social ramifications. Thus, focusing on the non-personalised (global) recommendations in news media platforms (e.g., top-k trending topics on Twitter, top-k news on a news platform, etc.), we discuss on two specific fairness concerns together (traditionally studied separately)---user fairness and organisational fairness. While user fairness captures the idea of representing the choices of all the individual users in the case of global recommendations, organisational fairness tries to ensure politically/ideologically balanced recommendation sets. This makes user fairness a user-side requirement and organisational fairness a platform-side requirement. For user fairness, we test with methods from social choice theory, i.e., various voting rules known to better represent user choices in their results. Even in our application of voting rules to the recommendation setup, we observe high user satisfaction scores. Now for organisational fairness, we propose a bias metric which measures the aggregate ideological bias of a recommended set of items (articles). Analysing the results obtained from voting rule-based recommendation, we find that while the well-known voting rules are better from the user side, they show high bias values and clearly not suitable for organisational requirements of the platforms. Thus, there is a need to build an encompassing mechanism by cohesively bridging ideas of user fairness and organisational fairness. In this abstract paper, we intend to frame the elementary ideas along with the clear motivation behind the requirement of such a mechanism.

Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

MACHINE LEARNING BASED STUDENT PERFORMANCE ANALYSIS SYSTEM

The academic output of the student is normally stored in various formats in the student administration system (files, documents, records, photographs and other formats). These data can be collected for valuable knowledge from the students. However, it is difficult to analyze the increasing amount of data of students through conventional statistical techniques and database management tools. For universities to gather valuable information, a tool is therefore required. This helpful knowledge can be used to predict the success of students. Leistungs analyze learning results is a framework that aims for success in the areas of student interest at various levels and dimensions. This paper proposes a complete structure as a rule-based recommendation method not only for analyzing and forecasting the success of students but also for presenting their reasons. The proposed system analyzes demographic details for pupils, studies and psychological features so that students, teachers and parents can collect all possible knowledge. To seek maximum accuracy in academic predictions across a range of powerful techniques of data mining. The system successfully recognizes the limitations of the student and makes adequate recommendations. The practical case study on 200 students indicates the excellent performance of the proposed system compared to the current framework.

Big data analytics of social network marketing and personalized recommendations

A fan page is a kind of a social network. Social network marketing (SNM) is a form of Internet marketing involving the creation and sharing of content on social media networks to achieve marketing and selling goals. In addition, precise SNM requires sufficient data and analysis in terms of making accurate online recommendations. This study examines the experience of various Taiwanese fan page users utilizing a market survey, a total of 1032 valid questionnaire data, and the questionnaire is divided into five sections with 33 items in terms of a big data structure based on a relational database on the first research stage. All questions use nominal and ordinal scales. In the second stage, this study develops a personalized recommendation system (PRS) using big data analytics approach, including cluster analysis and association rules. This study shows how the research results can obtain fans behavior knowledge by examining different group profiles and develop rule-based recommendation approach to generate personalized recommendations for building a SNM mechanism.

Investigating the Behaviors of Mobile Games and Online Streaming Users for Online Marketing Recommendations

Mobile games have become online leisure and communication activities, so in recent years, different generation players have gradually become more involved in mobile games and the market has grown. Online streaming refers to entertainment and communicate in the form of a real-time broadcast on the internet, such as mobile gaming apps which create a one-stop game audio-visual interactive online marketing and social media/network platforms. Using a market survey on Taiwan players, a total of 1,020 questionnaires data are incorporated into a relational database. Big data analytics, including cluster analysis and association rules, are used to determine game players' profiles and their preference patterns and rules. Using cluster analysis, the authors divide Taiwan mobile game players into three clusters and then find each group's profiles. In addition, this study develops a rule-based recommendation approach, association rules, for investigating mobile games players' online streaming and purchasing behaviors in terms of online marketing recommendations.

Rule-based Recommendation System based on Semantic Web of Things

Rule-based Recommendation System based on Semantic Web of Things

On Scalability of Association-rule-based Recommendation

The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users’ actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.

AMLVaran: a software approach to implement variant analysis of targeted NGS sequencing data in an oncological care setting

BackgroundNext-Generation Sequencing (NGS) enables large-scale and cost-effective sequencing of genetic samples in order to detect genetic variants. After successful use in research-oriented projects, NGS is now entering clinical practice. Consequently, variant analysis is increasingly important to facilitate a better understanding of disease entities and prognoses. Furthermore, variant calling allows to adapt and optimize specific treatments of individual patients, and thus is an integral part of personalized medicine.However, the analysis of NGS data typically requires a number of complex bioinformatics processing steps. A flexible and reliable software that combines the variant analysis process with a simple, user-friendly interface is therefore highly desirable, but still lacking.ResultsWith AMLVaran (AML Variant Analyzer), we present a web-based software, that covers the complete variant analysis workflow of targeted NGS samples. The software provides a generic pipeline that allows free choice of variant calling tools and a flexible language (SSDL) for filtering variant lists. AMLVaran’s interactive website presents comprehensive annotation data and includes curated information on relevant hotspot regions and driver mutations. A concise clinical report with rule-based diagnostic recommendations is generated.An AMLVaran configuration with eight variant calling tools and a complex scoring scheme, based on the somatic variant calling pipeline appreci8, was used to analyze three datasets from AML and MDS studies with 402 samples in total. Maximum sensitivity and positive predictive values were 1.0 and 0.96, respectively. The tool’s usability was found to be satisfactory by medical professionals.ConclusionCoverage analysis, reproducible variant filtering and software usability are important for clinical assessment of variants. AMLVaran performs reliable NGS variant analyses and generates reports fulfilling the requirements of a clinical setting. Due to its generic design, the software can easily be adapted for use with different targeted panels for other tumor entities, or even for whole-exome data. AMLVaran has been deployed to a public web server and is distributed with Docker scripts for local use.

A model of concept hierarchy-based diverse patterns with applications to recommender system

Frequent pattern mining is one among the popular data mining techniques. Frequent pattern mining approaches extract interesting associations among the items in a given transactional database. The items of the transactional database can be organized as a concept hierarchy. Notably, frequent pattern mining does not distinguish the patterns by analyzing the categories of the items in a given concept hierarchy. In several applications, it is often useful to distinguish among the frequent patterns by analyzing how the items of the pattern are mapped to different categories of the concept hierarchy. In this paper, we propose a new interestingness measure, designated as diversity rank (drank), for capturing the diversity of a given pattern by analyzing the extent to which the items of the pattern are associated with the categories of the corresponding concept hierarchy. Given a transactional database over a set I of items and the corresponding concept hierarchy on I, we propose a methodology to compute the drank of the given pattern. Furthermore, by extending the notion of drank, we propose an approach to improve the diversity and accuracy of association rule-based recommender system. The results of our performance evaluation on the real-world MovieLens dataset demonstrate that the proposed diversity model extracts different kinds of patterns as compared to frequent patterns. Furthermore, our proposed recommender system approach improves the diversity performance w.r.t. the existing association rule-based recommender system without significantly compromising the accuracy. Overall, the proposed concept hierarchy-based diverse pattern model provides a scope to develop new approaches for improving the performance of frequent pattern mining-based applications.

A Survey of Ontology Benchmarks for Semantic Web Ontology Tools

Software engineering employs different benchmarks for a software evaluation. This enables software developers to continuously improve their product. The same needs are intrinsic for software tools in the semantic web field. While there are many different benchmarks already available, there has not been their overview and categorization yet. This work provides such an overview and categorization of benchmarks specifically oriented on benchmarks where an ontology plays an important role. Benchmarks are naturally categorized in line with ontology tool categorization along with an indication which activities those benchmarks are deliberate and which are non-deliberative. Although the article itself can already navigate a reader to an adequate benchmark, we moreover automatically designed a flexible rule-based recommendation tool based on the analysis of existing benchmarks.

Ontology and Rule-Based Recommender System for E-learning Applications

The continuous growth of the internet has given rise to an overwhelming mass of learning materials. Which has increased the demand for a recommendation system to filter information and to deliver the learning materials that fit learners learning context. In this paper, we propose an architecture of a semantic web based recommender system. The proposed architecture is a redesigned architecture of the classical 3-tiers web application architecture with an additional semantic layer. This layer holds two semantic subsystems: an Ontology-based subsystem and SWRL (Semantic Web Rule Language) rules one. The Ontology subsystem is used as a reusable and sharable domain knowledge to model the learning content and context. The SWRL rules are used as a recommendation and filtering technique based on learning object relevance and weighting. These rules are organized into four categories: Learning History Rules (LHR), Learning Performance Rules (LPR), Learning Social Network Rules (LSNR) and Learning Pathway Rules (PR).