Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval

Tawfik, Andrew A.; Alhoori, Hamed; Keene, Charles Wayne; Bailey, Christian; Hogan, Maureen

doi:10.1007/s10758-017-9335-y

Cited by 6 publications

(6 citation statements)

References 43 publications

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“…Berdasarkan dari hasil analisis dan pengujian data untuk memprediksi keanekaragaman preferensi mendengarkan musik yang menggunakan algoritma K-Nearest, Naive Bayes, Decision tree dan ensemble method serta linear regression maka dapat disimpulkan bahwa Algoritma Naive Bayes memiliki tingkat akurasi yang lebih baik dibandingkan algoritma lainnya dengan target Looking back at your choices, Indicate what influenced you the most (2). Rata rata nilai algoritma Naive Bayes dari lima algoritma lainnya yaitu sebesar 87%.…”

Section: Kesimpulanunclassified

“…Metode CBR,memanfaatkan pengalaman sebelumnyauntuk memecahkan masalah baru, adalah cara yang efektif untuk mendukung pemecahan masalah. Teori ini menyatakan bahwa ketika pembelajar dihadapkan pada masalah yang kompleks, mereka dapat menggunakan kasus-kasus sebelumnya untuk menginterpretasikan situasi baru dan memperoleh solusi [2]. Permasalahan yang ditemui dalam penelitian sebelumnya yang dilakukan oleh G. A. V. Mastrika Giri [15] adalah nilai precision berdasarkan preferensi pengguna tidak tinggi, hanya 66%.…”

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Penerapan Algoritma Supervised Learning untuk Klasifikasi Data Music Listening

Mardiani,

Rahmansyah,

Kurniati

et al. 2023

MALCOM

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Perkembangan teknologi informasi semakin pesat hingga saat ini terus dikembangkan teknologi-teknologi terbaru, penggunaan data sebagai pengolahan kini semakin banyak diterapkan pada berbagai bidang, dengan penggunaan data dapat dilakukan perkembangan teknologi yang dapat meningkatkankan kualitas dan efisiensi, pengolahan data dapat diterapkan di semua bidang dan salah satunya adalah di bidang musik. Musik sudah menjadi bagian dari kehidupan manusia sehari-hari. Sudah banyak media-media yang menyajikan berbagai macam lagu dengan genre yang beragam pula mulai dari Pop, Jazz, Rock, R&B, dan genre-genre lainnya. Dengan menggunakan data mining untuk mengolah data maupun menganalisis data-data musik kita dapat memprediksi keanekaragaman preferensi mendengarkan musik dan dengan menggunakan tools aplikasi Orange Data Mining dapat membantu masyarakat untuk mengetahui musik apa saja yang diminat dengan memprediksi melalui beberapa metode dalam tools orange, dengan Algoritma Naive Bayes memiliki tingkat akurasi yang lebih baik.

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Section: Kesimpulanunclassified

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Penerapan Algoritma Supervised Learning untuk Klasifikasi Data Music Listening

Mardiani,

Rahmansyah,

Kurniati

et al. 2023

MALCOM

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“…It has been widely employed to address various real-world problems. For example, knowledge graph-based reasoning can be used in speech recognition to parse speech contents into logical propositions [156], and case-based reasoning can be adopted to address the data-sparsity issue in recommendation systems by filling in the vacant ratings of the user-item matrix [157]. A typical reasoning method is based on the Belief, Desire and Intention (BDI) model [158].…”

Section: Multi-agent Reasoningmentioning

confidence: 99%

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Zhu¹,

Ye²,

Wang³

et al. 2022

IEEE Trans. Knowl. Data Eng.

105

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Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

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“…In the state of the art, this approach has been approached mainly through two techniques of Artificial Intelligence: Expert Systems and CBR, the latter being the technique that will be used in this work. For example, in [49], CBR is used for the selection and recommendation of pedagogical strategies, from an initial set of strategies stored in the student's model, considering the particular characteristics of each individual, such as personality profiles, multiple emotions and intelligences, and cognitive processes of the students, within virtual learning systems [49]; while in [50], the assumption is considered that "learners struggle to identify and retrieve the optimal case to solve a new problem", and proposes a CBR-based recommendation system to "support the decision-making process about which case is most relevant to solve new problems".…”

Section: E Case Based Reasoning (Cbr) and Recommendation Systems (Rs)mentioning

confidence: 99%

Using Project-based Learning in a Hybrid e-Learning System Model

Alfaro¹,

Rivera²,

Luna-Urquizo

2019

IJACSA

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After conducting the historical review and establishing the state of the art, the authors of this paper focus on the incorporation of Project Based Learning (PBL), in an adaptive e-Learning environment, a novel and emerging perspective, which allows the application of what today constitutes one of the most effective strategies for the process of teaching learning. In PBL, each project is defined as a complex task or problem of reality, for which resolution, the student must develop research activities, planning, design, development, validation, testing, etc. For the proposal of the Hybrid Architecture of the e-Learning system model, the authors use artificial intelligence techniques, which make it possible to identify the Learning Styles (LS), with the purpose of automatically assigning the projects, according to the characteristics, interests, expectations and demands of the student, who will interact with an e-Learning environment, with a high capacity of adaptation to each individual. Finally, the conclusions and recommendations of the research work are established.

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Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval

Cited by 6 publications

References 43 publications

Penerapan Algoritma Supervised Learning untuk Klasifikasi Data Music Listening

Penerapan Algoritma Supervised Learning untuk Klasifikasi Data Music Listening

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Using Project-based Learning in a Hybrid e-Learning System Model

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