Automatic Recommendation System Research Articles

An ontology-based knowledge representation using OWL for Indonesian local regulations

<div class="WordSection1"><p>Inconsistencies in legislation can significantly hinder the effectiveness and efficiency of the central and local government administrations. The Indonesian government requires standard harmonization of each piece of legislation to prevent such problems. However, this process is often manual and requires the involvement of multiple experts with varying backgrounds. This leads to high resource expenses regarding human resources, cost, and time. To address this, a software system should be developed to detect potential disharmony among legislation. However, the system requires a well-constructed legislation conceptual model represented in an appropriate modeling language. This research aims to develop the Indonesian local regulation ontology in web ontology language (OWL), where no such ontology exists. The ontology was created using the Ontology Development 101 methodology and evaluated using competency questions and expert judgment approaches. The resulting ontology becomes a basis for developing an automatic recommendation system to detect potentially inconsistent legislation in future works.</p></div>

Investment Recommender System Model Based on the Potential Investors' Key Decision Factors.

In this research, we propose an automatic recommender system for providing investment-type suggestions offered to investors. This system is based on a new intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS) that works with four potential investors' key decision factors (KDFs), which are system value, environmental awareness factors, the expectation of high return, and expectation of low return. The proposed system provides a new model for investment recommender systems (IRSs), which is based on the data of KDFs, and the data related to the type of investment. The solution of fuzzy neural inference and choosing the type of investment is used to provide advice and support the investor's decision. This system also works with incomplete data. It is also possible to apply expert opinions based on feedback provided by investors who use the system. The proposed system is a reliable system for providing suggestions for the type of investment. It can predict the investors' investment decisions based on their KDFs in the selection of different investment types. This system uses the K-means technique in JMP for preprocessing the data and ANFIS for evaluating the data. We also compare the proposed system with other existing IRSs and evaluate the system's accuracy and effectiveness using the root mean squared error method. Overall, the proposed system is an effective and reliable IRS that can be used by potential investors to make better investment decisions.

How Optimal Transport Can Tackle Gender Biases in Multi-Class Neural Network Classifiers for Job Recommendations

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can, however, be used in applications that are ranked as High Risk by the European Commission in the AI act—for instance, online job candidate recommendations. When used in the European Union, commercial AI systems in such applications will be required to have proper statistical properties with regard to the potential discrimination they could engender. This motivated our contribution. We present a novel optimal transport strategy to mitigate undesirable algorithmic biases in multi-class neural network classification. Our strategy is model agnostic and can be used on any multi-class classification neural network model. To anticipate the certification of recommendation systems using textual data, we used it on the Bios dataset, for which the learning task consists of predicting the occupation of female and male individuals, based on their LinkedIn biography. The results showed that our approach can reduce undesired algorithmic biases in this context to lower levels than a standard strategy.

Mining Competitors and Finding Winning Plans Using Feature Scoring and Ranking-Based CMiner++ Algorithm

For a business to succeed, it is very important to make things speaking more to clients than to rivals. It is more critical to decide on the significant feature of an item which influences its competency. In spite of the works that have been done already, a few algorithms gained efficient solution. This paper proposes the CMiner++ Algorithm to assess the competitive relationship among items in unstructured dataset and finding the Top-K competitors of a given item. Definitively, the nature of the outcomes and the versatility of this methodology utilizing numerous datasets from various areas are assessed, and the efficiency and adaptability of this algorithm on various data sets are improved when compared to existing algorithms. In today's busy world, automatic recommendation systems are emerging because people are looking for the products best suited for them. So, it is very important to analyse the behaviour of people, make a review on large and large unstructured data sets, and make the fully automated deep learning system to ensure the accurate outcome.

Recommendation System with Biclustering

The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants. One of the most frequently used recommendation methods is collaborative filtering, but its accuracy is limited by the sparsity of the rating dataset. Most existing collaborative filtering methods consider all features when calculating user/item similarity and ignore much local information. In collaborative filtering, selecting neighbors and determining users' similarities are the most important parts. For the selection of better neighbors, this study proposes a novel biclustering method based on modified fuzzy adaptive resonance theory. To reflect the similarity between users, a new measure that considers the effect of the number of users' common items is proposed. Specifically, the proposed novel biclustering method is first adopted to obtain local similarity and local prediction. Second, item-based collaborative filtering is used to generate global predictions. Finally, the two resultant predictions are fused to obtain a final one. Experiment results demonstrate that the proposed method outperforms state-of-the-art models in terms of several aspects on three benchmark datasets.

Study of the User Behaviour Caused by Automatic Recommendation Systems Call to Action.

Diagnostics accuracy and usability of symptom checkers have been researched in several studies. Their ability to set a correct diagnosis especially in the urgent cases is questionable. There is one aspect of symptom checkers that has not been deeply studied yet. It is their ability to motivate patients to follow up after receiving a direct recommendation and to decrease a load on the health care professionals. The goal of this research is to study how patients behave after receiving a recommendation from a symptom checker and motivation of this behavior. We studied how patients react on the symptom checker recommendations and the motivation behind this behavior. In total we invited 3615 patients to have a symptom checker screening; 2374 of them agreed to run a symptom checker screening; 867 of them agreed to participate in the study. The proportion of the patients who agreed to have a symptom checker screening. So, we can clearly see that symptom checker screening doesn't result in a significant decrease of the load on healthcare professionals. This is supported by the quantitative study results. The patients emphasized the ease of use of the tool and clearness of the recommendations it gives. However, they perceived it as rather a second opinion tool or a tool that helps to prepare to the doctor's visit.

A Model Text Recommendation System for Engaging English Language Learners: Facilitating Selections on CEFR

A pedagogically informed multimodal education system is defined by how well reading tasks are assigned to students in a contemporary classroom. A source that becomes a provider of readings is the web, where it is possible to find information on practically all areas of knowledge and in a wide variety of languages. However, selecting the appropriate material for the level and theme becomes a tedious job to which language teachers must devote a significant amount of their time. Selecting suitable readings to accompany the teaching-learning process is thus not a ‘trivial’ task. Basic-level texts for language competence are easy to recognize and obtain but as is seen in the case of the Common European Framework of Reference for Languages recommendations (CEFR), selection of appropriate texts that impart language competencies, especially of vocabulary and grammar at higher levels of communicativeness, selection becomes increasingly complex for teachers. Furthermore, the suggested readings should be raked by complexity in accordance with student capabilities. We suggest, that automatic classifiers based on CEFR levels may help in this process of selections from the already available corpora of authentic texts on the web. The existing facility of access of readers to such material on the web may come to the aid of automated classifiers. Teachers use interest to motivate reading in classrooms, but automatic recommendation systems will allow specific or even individualized recommendations. The authors explore the impact of such multimodal methods on the acquisition of better linguistic and communicative skills.

The robustness of Random Forest and Support Vector Machine Algorithms to a Faulty Heart Sound Segmentation.

Cardiac auscultation is the key exam to screen cardiac diseases both in developed and developing countries. A heart sound auscultation procedure can detect the presence of murmurs and point to a diagnosis, thus it is an important first-line assessment and also cost-effective tool. The design automatic recommendation systems based on heart sound auscultation can play an important role in boosting the accuracy and the pervasiveness of screening tools. One such as step, consists in detecting the fundamental heart sound states, a process known as segmentation. A faulty segmentation or a wrong estimation of the heart rate might result in an incapability of heart sound classifiers to detect abnormal waves, such as murmurs. In the process of understanding the impact of a faulty segmentation, several common heart sound segmentation errors are studied in detail, namely those where the heart rate is badly estimated and those where S1/S2 and Systolic/Diastolic states are swapped in comparison with the ground truth state sequence. From the tested algorithms, support vector machine (SVMs) and random forest (RFs) shown to be more sensitive to a wrong estimation of the heart rate (an expected drop of 6% and 8% on the overall performance, respectively) than to a swap in the state sequence of events (an expected drop of 1.9% and 4.6%, respectively).

Abstract LB502: Too many bioinformatics tools? Machine learning enables automatic recommendation on variant callers for long-read sequencing data

Abstract During the past decade, hundreds of bioinformatics tools were developed and released for detecting structural variants from the next generation sequencing and the third generation sequencing (e.g. PacBio, Nanopore) data. Each tool claimed its specific advantages comparing to others. Some tools present their efficiency on accurately identifying indels, while some others are good at detecting large and complicated structural variants. For a data analyst, especially for those have limited expertise on sequential algorithms, how to choose a suitable variant caller that fits for the owned data? To facilitate the data analysis, here, we developed and presented an automatic recommendation system. When the system is given a sequencing data (BAM/SAM file), it suggests a variant caller that may perform best, from a set of state-of-the-art bioinformatics tools. The proposed recommendation system is developed under a machine learning framework, which is called meta-learning technique. The meta-learning method enables the system to identify the associations between the features from data and the performance of tools. To achieve this, the meta-learning method consists of the following two steps: First, the method (1) extracts the metadata from the training dataset. Here, the metadata includes meta-feature collection and meta-target identification. When the training dataset is large enough, a comprehensive meta-database is obtained. Then, it (2) constructs a recommendation model according to the meta-database. As a tool has only two recommendation status, the recommendation model can be built by a classification approach. We adopted RandomForest in the system. When a new sequencing data is given to the system, it applies meta-feature collection on the data, then the recommendation is offered according to the classification results mainly on meta-features. In addition, different scenarios may have different preference on variant calling performance. We considered 3 meta-targets, which are F-measure, precision and recall. When F-measure is set to meta-target, the variant caller that may present highest F-measure is recommended. The settings for the precision and recall are similar, respectively. We conducted a series of experiments to test the validity of the proposed recommendation system. We selected 5 popular variant callers for PacBio sequencing data. The training data was generated by published simulator. The validation data included both the simulated data and the real data with benchmarks of structural variants. The experimental results indicated that different variant callers often fit for different data, while none of the callers seems efficient for every data. For the recommendation system, the F-measure and AUC could reach 76.90% and 90.80% in some tests, respectively and the recommendation accuracy kept more than 90% on average. Citation Format: Shenjie Wang, Xiaoyan Zhu, Yuqian Liu, Xiao Xiao, Jiayin Wang. Too many bioinformatics tools? Machine learning enables automatic recommendation on variant callers for long-read sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB502.

Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

With the emergence of computers and networks, the social needs for English competence have presented a diversified and professionalized trend. The current single teaching model can no longer satisfy students’ needs. To cater to different demands of students and improve their level of satisfaction with personalized and automatically recommended teaching videos, an automatic recommendation sys-tem of college English teaching videos, which consists of interface layer, busi-ness logic layer, and data layer, was designed. The quality of recommended teaching videos was ensured through the strict management of English teaching videos within the system. The degree of interest in videos was calculated accord-ing to students’ browsing history of teaching videos. The content of teaching vid-eos that meet students’ personalized demands was established on the basis of the degree of interest. The Naïve Bayesian classification method was used to precise-ly, rapidly, and stably divide the teaching videos into two classes—interest and disinterest—according to the abovementioned information. Results show that the recall ratio and precision ratio of this system reach as high as 95.18% and 97.2%, respectively. The system recommendations averagely rank top, the recommenda-tion precision is high, and the recommended video contents are abundant, with an applause rate of 97.79%. This designed system can establish student-centered college English teaching methods, create a favorable language environment, and better promote the teaching of English among college students.

Automatic recommendation system based on hybrid filtering algorithm

Automatic recommendation system based on hybrid filtering algorithm

Recommendation as generalization: Using big data to evaluate cognitive models.

The explosion of data generated during human interactions online presents an opportunity for psychologists to evaluate cognitive models outside the confines of the laboratory. Moreover, the size of these online data sets can allow researchers to construct far richer models than would be feasible with smaller in-lab behavioral data. In the current article, we illustrate this potential by evaluating 3 popular psychological models of generalization on 2 web-scale online data sets typically used to build automated recommendation systems. We show that each psychological model can be efficiently implemented at scale and in certain cases can capture trends in human judgments that standard recommendation systems from machine learning miss. We use these results to illustrate the opportunity Internet-scale data sets offer to psychologists and to underscore the importance of using insights from cognitive modeling to supplement the standard predictive-analytic approach taken by many existing machine learning approaches. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Music similarity measurement and recommendation system using convolutional neural networks

Due to the massive and growing volume of music on the internet, and the lack of proper management on this massive volume, similarity and music recommendation systems have been designed. The music similarity system, which is the basis of the recommendation system, can automatically generate a user's playlist according to the similar features of each piece of music. In this paper, we designed a desirable music genre classification using convolutional neural network for extracting high-level features from intermediate networks layers. For similarity measurement, we considered cosine similarity and Euclidean distances between feature vectors. We applied this automatic recommendation system on three databases with different genres and showed that the recommender achieves significant accuracy in 10-Best results.

Influence of conditional holoentropy-based feature selection on automatic recommendation system in E-commerce sector

This paper presents a new contribution to the recommendation system following E-commerce reviews. Here, it implements a dictionary-based sentiment analysis based on four main steps (i) Pre-processing (ii) Semantic word extraction (iii) Feature Extraction (iv) Classification. Initially, the tweets or reviews extracted from the database are subjected to pre-processing, which involves three processes: stop word removal, stemming, and blank space removal. In the semantic word extraction process, the semantic words from the dictionary will be extracted by matching with the extracted keywords. The next process is the feature extraction, which extracts joint holoentropy and cross holoentropy of all keywords, and further proceeds with conditional holoentropy-based feature selection process. Finally, the selected features are subjected to a classification process using Deep Belief Network (DBN), which is a well-performing deep learning algorithm. Moreover, the main contribution of the paper relies on the improvement of DBN architecture, where the number of hidden neurons and activation function is optimally tuned by a hybrid optimization algorithm termed as Grey linked Chick Update-based Chicken Swarm Optimization (GCU-CSO). At last, the comparative analysis of proposed and conventional algorithms in terms of various performance metrics proves the efficacy of the proposed E-commerce recommendation system.

On the Usage of Pre-Trained Speech Recognition Deep Layers to Detect Emotions

One of the Industry 4.0 landmarks, concerns the optimization of manufacturing processes by increasing the operator’s productivity. But productivity is highly affected by the operator’s emotions. Positive emotions (e.g. happiness) are positively related to productivity, in contrast negative emotions (e.g. frustration) are negative related to productivity and positive related to misconducts and misbehaviors on the workplace. Thus perhaps, automatic recommendation systems can suggest actions or instructions to eliminate or attenuate undesired negative emotions on the workplace. These systems might support their actions based on the reliability of emotion detectors. In this paper, emotions are detected thought a speech system. Our solution was built over deep speech recognition layers, namely the first two convolutional layers of the pre-trained 2015 Baidu’s speech recognition model. In re-utilizing these first two convolutional layers, robust meta-features are expected to be extracted. Our deep learning model attempts to predict the seven primary emotions on the MELD test set.Furthermore, our solution did not use any contextual data and yet it achieved robust results. The proposed weighted TrBaidu algorithm achieved state-of-art results on the detection of joy and surprise emotions, a F1-score rate of 23 % for both emotions.

Automatic recommender algorithm of reviewers using machine learning

어떤 학술단체의 학술지에 소속된 연구자가 논문을 투고하는 경우 관련된 연구전문가들의 심사를 받고 게재여부가 판가름된다. 이때 학술지 책임자(편집위원장) 입장에서는 심사위원들의 전공 pool을 참고하여 주관적으로 심사위원을 배정하게 되는데, 이에 대한 배정이 연구주제와 관련하여 제대로 배정이 되었는지 타당성 문제가 발생하는 경우가 많다. 왜냐하면, 연구자들의 세부전공 분야는 끊임없이 변화하기 때문에 초기 입력된 전공 pool 정보만으로는 정확한 심사 배정에 한계를 가지게 된다. 본 연구에서는 이와 같은 심사위원 배정 혹은 평가위원 배정 문제를 머신러닝 기법을 활용하여 관련 연구자들의 연구주제 그룹 pool을 새롭게 생성하고 이로부터 무작위 배정을 자동으로 실행하는 심사위원 자동추천 알고리즘을 제안하고자 한다. 여기서 제안하는 자동추천 알고리즘 타당성을 확인하기 위해서 머신러닝 기법 중 비지도 학습알고리즘인 k-평균 군집방법을 이용하여 문서를 군집화하고, 그 결과를 이용하여 군집 소속정보를 원 데이터에 라벨링(labelling)하였다. 그다음 단계로 라벨링된 데이터를 텍스트분류에 많이 사용하는 나이브베이즈방법을 이용하여 문서(논문제목) 분류 모형을 개발하였으며, 평가용 데이터에 적용해본 결과 1.1%의 오분류을 보여주었다.In case of submitting someone’s manuscript, accepting for publication depends on peer reviewers’ decisions. The Editor of jounal chooses the related reviewers with considering their major fields. However, in this process, validation of allocating reviewers is often a problem because researcher’s major fields are changing in time. In this study, we propose the automatic recommender system of reviewers using machine learning techniques. To confirm the validity of our recommender system, first we cluster the documents using k-means clustering and then labelled the cluster information to the raw data. After labelling, we develop the text(thesis’ title) classification model using the naive bayes method. As a result of adopting our model to validation set, we found the misclassification error was 1.1%.

Beyond User Needs: A Meaning‐Oriented Approach to Recommender Systems

This contribution is a case study of Spotify, a popular music streaming app, which uses automated recommendations to provide a better user experience to its listeners. Automated recommender systems have mostly been built around understanding user needs and user goals. Our case study presents a meaning‐oriented approach aimed at understanding what users regard as meaningful and how an automated recommender system can forge meaning and offer experiences that help develop existing connections to music and generate new ones.Following the meaning‐oriented approach inspired by Lucien Karpik (2010), we were able to better understand how different audience segments engage with music and experience music as meaningful. We identified 2 cultural engagement models that listeners use to relate to music: (1) musical engagement during which music is the focus of the experience; and (2) non‐musical engagement, during which the listener is the focus of the experience. Each engagement model uses different types of cognitive and evaluative aids, which we refer to as cues and proof points, to derive meaning from listening experiences. We also identified nine distinct types of experiences of meaning defined by distinct types of cues and proof points.The proposed approach is applicable to the study and innovation of experience‐led digital platforms and recommender systems.

Effect of automation transparency in the management of multiple unmanned vehicles

Automated decision aids can undoubtedly benefit system performance, but have the potential to provide incorrect advice, creating the possibility for automation disuse or misuse. This paper examined the extent to which increased automation transparency could improve the accuracy of automation use in a simulation of unmanned vehicle control. Participants were required to assign the best unmanned vehicle to complete missions. An automated recommender system provided advice but was not always reliable. Three levels of automation transparency were manipulated between-participants. Providing transparency regarding the reasoning underlying automated recommendations improved the accuracy of automation use, with no cost to decision time or subjective workload. Higher transparency in the form of projected decision outcome visualizations led to faster decisions, but less accurate automation use and an automation bias. Implications for the design of transparent interfaces to improve human-autonomy teaming outcomes in time-pressured environments are discussed.

KG2Rec: LSH-CF recommendation method based on knowledge graph for cloud services

With the rapid development of cloud computing paradigm and mobile devices, the number of cloud services and user interest data are growing explosively. It becomes harder for users to find suitable cloud services within satisfying response time. Thus a suitable cloud service automatic recommendation system is needed to solve the above problem. In this work, we propose KG2Rec, a novel recommendation method based on Knowledge Graph to provide users with appropriate recommendations to meet their real-time needs. The property-specific knowledge graph model is designed to model user–item and item–item bipartite relations. Based on the property-specific knowledge graph model, property-specific user–item relation features can be mined from it. Then the user–item relation features will be integrated into an improved collaborative filtering algorithm based on the local sensitive hash technique for Top-N item recommendation. Finally, we evaluate the proposed method in terms of Top-N recommendation on the MovieLens dataset, and prove it outperforms numbers of state-of-the-art recommendation systems. In addition, we prove it has well performance in term of long tail recommendation and big data processing.

On automatic recommender algorithm with regularized convolutional neural network and IR technology in the self-regulated learning process

Automatic recommendation algorithm is prominent component of many of today’s IR spectral signal educational applications. The most effective approaches for recommendation algorithm are based on the deep learning technology. The recommender system plays an important role in the field of IR spectral signal recognition. However, the infrared spectrometers often exist the problem of the low signal-to-noise and overlapped peaks. To address those issues, we propose an IR spectrum signal reconstruction approach with the framelet transform regularization. First, the famous framelet transform is introduced to reveal the difference between the overlap IR spectral signal and the high-resolution one. We find that the framelet coefficient distribution of the high-resolution IR spectral signal is sparser than that of the overlap one. Furthermore, to optimize the developed method, the alternating-direction-method-of-multipliers is introduced to calculate the IR spectral signals and the overlap function. Simulated and real experiments on the IR spectral signals illustrate that the developed method outperform the existed popular algorithms. The proposed approach is applied in the automatic recommender system that is crucial for IR spectrum teaching in the self-regulated learning process.