Computer Sciences Commons^™

การเรียนรู้ถ่ายโอนสำหรับการจำแนกประเภทภาพเนื้อลายหินอ่อนเทียมด้วยโครงข่ายประสาทคอนโวลูชัน, เกรซ พานิชกรณ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในปัจจุบัน เทคนิคการประมวลผลภาพถูกนำมาใช้กันอย่างแพร่หลายในหลากหลายอุตสาหกรรม หนึ่งในนั้นคือการควบคุมคุณภาพที่ใช้ในอุตสาหกรรมการผลิตอาหาร ขณะเดียวกัน หนึ่งในปัญหาที่การเรียนรู้เชิงลึกสามารถนำมาใช้ตอบโจทย์ได้ดีเยี่ยมคือปัญหาการจำแนกรูปภาพ ในมุมมองของการเรียนรู้เชิงลึก ปัญหาที่หลากหลายของการจำแนกประเภทภาพสามารถแก้ไขได้อย่างรวดเร็วผ่านการเรียนรู้ถ่ายโอน งานวิจัยนี้จึงนำเสนอวิธีการประยุกต์ใช้เทคนิคการเรียนรู้ถ่ายโอนในการฝึกสอนแบบจำลองโครงข่ายคอนโวลูชันเชิงลึกเพื่อจำแนกภาพเนื้อลายหินอ่อนเทียมหรือเป็นเนื้อลายหินอ่อนแท้ แบบจำลองที่เทรนมาเรียบร้อยแล้วสามแบบจำลอง ประกอบด้วย วีจีจี16 เรสเน็ต50 และ อินเซปชันวี3 ได้ถูกเลือกมาใช้ในการทดลองเพื่อสร้างแบบจำลองทั้งหมด 4 ตัว ประกอบด้วย ซีเอ็นเอ็นปกติ ซีเอ็นเอ็น+วีจีจี16 ซีเอ็นเอ็น+เรสเน็ต50 และ ซีเอ็นเอ็น+อินเซปชันวี3 พบว่า สมรรถนะแบบจำลองซีเอ็นเอ็น+อินเซปชันวี3 ให้ผลลัพธ์ที่ดีที่สุด จึงถูกเลือกนำไปปรับละเอียด การประเมินผลบนชุดข้อมูลทดสอบของแบบจำลองซีเอ็นเอ็น+อินเซปชันวี3ภายหลังการปรับแต่งให้ผลลัพธ์ค่าความแม่นยำที่ดีที่สุดคือ 96.7% เห็นได้ว่า แนวทางการจำแนกประเภทภาพที่นำเสนอมีความหวังสามารถนำไปพัฒนาต่อยอดเพื่อเป็นประโยชน์ต่อผู้ซื้อในการตรวจสอบเนื้อลายหินอ่อนเทียมที่อาจตั้งราคาสูงเกินจริง อันเป็นผลมาจากการฉีดไขมันให้มีลายมากมายสวยงาม ซึ่งจะทำให้เนื้อมีรสชาติดีขึ้นรวมทั้งสามารถตั้งราคาที่สูงขึ้นได้อีกด้วย

ระบบเพื่อตรวจสอบข่าวของการบริการข้อมูลจราจรทางอากาศระหว่างประเทศ, ศุภชัย เจียมวิจิตรกุล

Chulalongkorn University Theses and Dissertations (Chula ETD)

ระบบที่ใช้สนับสนุนข้อมูลในการทำงานของเจ้าหน้าที่ควบคุมจราจรทางอากาศ (Air Traffic Controller, ATC) ทำงานในส่วนของการส่งต่อความรับผิดชอบของเครื่องบินระหว่างประเทศ (ข่าว AIDC) พร้อมแสดงข้อมูลที่สำคัญ ปัจจุบันถ้าเกิดการส่งข่าว AIDC ไม่ครบทุกขั้นตอน ATC ต้องส่งต่อความรับผิดชอบผ่านทางโทรศัพท์ ซึ่งอาจจะเกิดความผิดพลาดจากเจ้าหน้าที่ งานวิจัยชิ้นนี้นำข่าว AIDC มาทำการตัดคำจากข้อความโดยใช้การกำหนดรูปแบบเพื่อค้นหากลุ่มคำ แล้วนำผลที่ได้มาตรวจสอบความครบถ้วนของกระบวนการการส่งข่าว AIDC ช่วยให้ ATC และ วิศวกรสามารถรับทราบรายละเอียดของเครื่องบินทุกลำ เพื่อประสานงานกับต่างประเทศได้ในทันที แม้ว่ากระบวนการถ่ายโอนความรับผิดชอบจะไม่สมบูรณ์ก็ตาม ผลจากงานวิจัยชิ้นนี้ช่วยให้สามารถแจ้งเตือน ATC และวิศวกรผู้เกี่ยวข้อง พร้อมบอกรายละเอียดได้อย่างถูกต้อง 100%

สมาร์ทฮับบนพื้นฐานของการติดต่อสื่อสารกับคอมพิวเตอร์ด้วยสมองโดยอุปกรณ์ราคาประหยัด, นิธิกร เกษมมงคลชัย

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิทยานิพนธ์นี้ได้ออกแบบและสร้างอุปกรณ์สมาร์ทฮับแบบพกพาบนพื้นฐานของการติดต่อสื่อสารกับคอมพิวเตอร์ด้วยสมองด้วยอุปกรณ์ราคาประหยัด โดยมีวัตถุประสงค์เพื่อออกแบบและประเมินวิธีการควบคุมอุปกรณ์ด้วยสัญญาณสมอง โดยสมาร์ทฮับที่กล่าวมานั้นเป็นส่วนหนึ่งของระบบสมาร์ทโฮม จากการศึกษางานวิจัยที่เกี่ยวข้องพบว่างานส่วนใหญ่ได้ใช้วิธีการควบคุมอุปกรณ์ด้วยสัญยาณสมองโดยใช้การกระพริบตาและระดับค่าความสนใจ จากการศึกษางานวิจัยที่เกี่ยวข้องได้ข้อสรุปว่าในงานวิทยานิพนธ์นี้จะใช้ระบบเครือข่าย WiFi สำหรับการสื่อสารระหว่างอุปกรณ์ และ จะใช้การกระพริบตาและระดับค่าความสนใจในการควบคุมเป็นหลัก ในการทดลองของงานวิทยานิพนธ์นี้มีผู้ทดลองทั้งหมด 10 คน โดยจะแบ่งเป็น 2 กลุ่ม โดยกลุ่มที่ 1 จะทดลองโปรแกรมที่ 1 ถึง 5 และ โปรแกรมที่ 2 จะทดลองโปรแกรมที่ 5 ถึง 1 จากผลการทดลองพบว่าค่าความแม่นยำในการใช้ค่าความสนใจนั้นมีค่ามากกว่าการกระพริบตา 2 ครั้ง แต่การใช้ค่าความสนใจในการควบคุมจะใช้เวลามากกว่า จากการทดลองสามารถสรุปได้ว่า ในการออกแบบวิธีการควบคุมสมาร์ทฮับบนพื้นฐานของการติดต่อสื่อสารกับคอมพิวเตอร์ด้วยสมองให้มีประสิทธิภาพมากที่สุดนั้นควรใช้ 1 วิธีการควบคุม ต่อ 1 คำสั่ง และ วิธีการควบคุมไม่ควรมีความซับซ้อนมากจนเกินไป

Multi-Evidence Learning For Medical Diagnosis, Tongjai Yampaka

Chulalongkorn University Theses and Dissertations (Chula ETD)

In recent years, a great many approaches for learning from multiple sources by considering the diversity of different views have been proposed. The most interesting field is medical diagnosis. For example, breast cancer screening normally employs two views of mammography (Cranio-Caudal and Medio-Lateral-Oblique) or two modes of ultrasound (B-mode and Doppler mode) breast images. This study proposes a multi-evidence learning model that combines the multiple evidences of breast images to improve diagnosis. Two views mammography and two modes of ultrasound were used. Our proposed model consists of four stages. First, feature extraction using Convolutional Neuron Networks was operated to extract …

Artificial Intelligence And Copyright Law In Singapore A Study On The Protection Of Compilations And Databases Arranged By Ai-Systems, Sella Say

Chulalongkorn University Theses and Dissertations (Chula ETD)

While the capability of artificial intelligence ("AI") gains remarkable momentum in creating copyrightable materials – the questions regarding the eligibility of these new creations, at the moment, are broadly discussed and posed challenges to the regime. The problem of how we fit the conventional notion of authorship and the condition of originality for AI-generated works remains a controversial topic. Some might suggest that subject matter created by AI should not be granted copyright protection on the presumption that AI is not a human who could treat as authors of works. At the same time, other supportive claims that the first …

A Robust System For Core Thai Natural Language Processing Technologies, Can Udomcharoenchaikit

Chulalongkorn University Theses and Dissertations (Chula ETD)

As the amount of unstructured textual data grows, it becomes increasingly important to build an intelligent system that can process it. Natural Language Processing (NLP) is a technology that allows a computer to exploit human languages to perform tasks. Deep learning models have shown excellent results across fundamental tasks in NLP, such as word segmentation, part-of-speech tagging, and named-entity recognition. However, in many situations, these proposed methods fail to perform well. For an NLP system to be robust, it must address issues such as out-of-vocabulary and spelling-mistakes. This thesis's research goal is to develop NLP models that can handle malformed …

Semi-Supervised Thai Sentence Segmentation Using Local And Distant Word Representations, Chanatip Saetia

Chulalongkorn University Theses and Dissertations (Chula ETD)

A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, …

Deep Sequential Real Estate Recommendation Approach For Solving Item Cold Start Problem, Jirut Polohakul

Chulalongkorn University Theses and Dissertations (Chula ETD)

The item cold-start problem occurs when a recommendation system cannot recommend new items owing to record deficiencies and new listing omissions. When searching for real estate, users can register a concurrent interest in recent and prior projects. Thus, an approach to recommend cold-start and warm-start items simultaneously must be determined. Furthermore, unrequired membership and stop-by behavior cause real estate recommendations to have many cold-start and new users. This characteristic encourages the use of a content-based approach and a session-based recommendation system. Herein, we propose a real estate recommendation approach for solving the item cold-start problem with acceptable warm-start item recommendations …

A Real Estate Valuation Model Using Boosted Feature Selection, Kankawee Chanasit

Chulalongkorn University Theses and Dissertations (Chula ETD)

To estimate real estate values, a complex valuation model based on artificial neural network (ANN) has been established as a successful means in modern machine learning research, specifically when high-dimensional data are available. Unfortunately, the real estate data in many locations, such as Thailand, are quite limited in terms of features. Hence, it becomes mandatory to reduce the complexity using feature selection techniques. These techniques aim to improve performance by identifying significant factors and help decrease the computational overload and model construction. However, due to the lack of explicability and interpretability in ANNs, the analysis of input factors cannot be …

Explainable Stock Price Prediction Using Technical Indicators With Short Thai Textual Information, Kittisak Prachyachuwong

Chulalongkorn University Theses and Dissertations (Chula ETD)

A stock trend prediction has been in the spotlight from the past to the present. Fortunately, there is an enormous amount of information available nowadays. There were prior attempts that have tried to forecast the trend using textual information; however, it can be further improved since they relied on fixed word embedding, and it depends on the sentiment of the whole market. In this paper, we propose a deep learning model to predict the Thailand Futures Exchange (TFEX) with the ability to analyze both numerical and textual information. We have used Thai economic news headlines from various online sources. To …

Detection Of Wagyu Beef Sources With Image Classification Using Convolutional Neural Network, Nattakorn Kointarangkul

Chulalongkorn University Theses and Dissertations (Chula ETD)

Wagyu beef originated in Japan. However, there are many types of Wagyu beef in the market around the globe. Primary sources include Australia, USA, Canada and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor and tenderness. Observing that there are differences in flavor, texture, and quality between distinct sources of Wagyu. This research presents an AI-based approach to identify Wagyu beef sources with image classification. The input images were collected from reliable sources on the internet and augmented with DCGAN. Deep neural networks, CNN, was constructed to detect the marbled fat …

Learning Personally Identifiable Information Transmission In Android Applications By Using Data From Fast Static Code Analysis, Nattanon Wongwiwatchai

Chulalongkorn University Theses and Dissertations (Chula ETD)

The ease of use of mobile devices has resulted in a significant increase in the everyday use of mobile applications as well as the amount of personal information stored on devices. Users are becoming more aware of applications' access to their personal information, as well as the risk that these applications may unwittingly transmit Personally Identifiable Information (PII) to third-party servers. There is no simple way to determine whether or not an application transmits PII. If this information could be made available to users before installing new applications, they could weigh the pros and cons of having the risk of …

Using Automatic Speech Recognition To Assess Thai Speech Language Fluency In Montreal Cognitive Assessment (Moca), Pimarn Kantithammakorn

Chulalongkorn University Theses and Dissertations (Chula ETD)

The Montreal Cognitive Assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning where scores are based on the number of unique correct words produced by the test-taker. However, with different languages, it is possible that unique words may be counted differently. This study focuses on Thai as a language that differs from English in its type of word combination. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the language fluency test of the MoCA with Thai language support. The …

Sop Development For Erp/Software Project Management Of A Consulting Company, Piyanat Viengcome

Chulalongkorn University Theses and Dissertations (Chula ETD)

Presently, ERP/software consulting company has experienced in information gathering process from users in customer company which lead to insufficient to-be process development and vendor selection process. The unexpected results would be extra budget and high manual workload in organization. The possible root cause is uncleared procedure to proceed the project management in ERP/software implementation. The objective of this research is to develop the standard operating procedure for ERP/software project management of a consulting company concentrated in manufacturing industry in Thailand. The methodology starts with studying of competitors' approach from their websites. The ERP expert interview is the next step, 3 …

Accurate Surface Ultraviolet Radiation Forecasting For Clinical Applications With Deep Neural Network, Raksit Raksasat

Chulalongkorn University Theses and Dissertations (Chula ETD)

Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280-360 nm, maximum 320 nm), or narrow-band UV B irradiation (main emission spectrum 310-315nm, maximum 311nm). For patients who cannot access phototherapy centers, sun-bathing, or heliotherapy, can be a safe and effective treatment alternative. However, as sunlight contains the full range of UV radiation (290-400 nm), careful sun-bathing supervised by photodermatologist based on accurate UV radiation forecast is vital to minimize potential adverse effects. Here, using 10-year UV radiation …

Med-Asa Smart Task-Volunteer Matching System, Taweesin Wongpinkaew

Chulalongkorn University Theses and Dissertations (Chula ETD)

In the context of healthcare, volunteers play an important role in improving the patient's experience and lowering the operational cost. However, the process which facilitate their management is reported to be problematic. In this thesis, the problems of the current system is explored, and a potential solution of a new IT system is outlined. The system was tested for a duration of 2 month during the COVID-19 outbreak in Thailand. SUS and an in-depth interview was conducted in order to gauge the usability and the effectiveness the system. The time it takes for the volunteers to go through with the …

Invariance And Invertibility In Deep Neural Networks, Han Zhang

Theses and Dissertations

Machine learning is concerned with computer systems that learn from data instead of being explicitly programmed to solve a particular task. One of the main approaches behind recent advances in machine learning involves neural networks with a large number of layers, often referred to as deep learning. In this dissertation, we study how to equip deep neural networks with two useful properties: invariance and invertibility. The first part of our work is focused on constructing neural networks that are invariant to certain transformations in the input, that is, some outputs of the network stay the same even if the input …

Multi-Label Classification Models For Heterogeneous Data: An Ensemble-Based Approach., Jose Maria Moyano Murillo

Theses and Dissertations

In recent years, the multi-label classification gained attention of the scientific community given its ability to solve real-world problems where each instance of the dataset may be associated with several class labels simultaneously, such as multimedia categorization or medical problems.

The first objective of this dissertation is to perform a thorough review of the state-of-the-art ensembles of multi-label classifiers (EMLCs). Its aim is twofold: 1) study state-of-the-art ensembles of multi-label classifiers and categorize them proposing a novel taxonomy; and 2) perform an experimental study to give some tips and guidelines to select the method that perform the best according to …

Sparsity And Weak Supervision In Quantum Machine Learning, Seyran Saeedi

Theses and Dissertations

Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics, and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. As building quantum computers is nearing the era of commercialization and quantum supremacy, it is essential to think of potential applications that we might benefit from. Among many applications of quantum computation, one of the emerging fields is quantum machine learning. We focus on predictive models for binary classification and variants of Support Vector Machines that we expect to be …

Vietnamese Punctuation Prediction Using Deep Neural Networks, Thuy Pham, Nhu Nguyen, Hong Quang Pham, Han Cao, Binh Nguyen

Research Collection School Of Computing and Information Systems

Adding appropriate punctuation marks into text is an essential step in speech-to-text where such information is usually not available. While this has been extensively studied for English, there is no large-scale dataset and comprehensive study in the punctuation prediction problem for the Vietnamese language. In this paper, we collect two massive datasets and conduct a benchmark with both traditional methods and deep neural networks. We aim to publish both our data and all implementation codes to facilitate further research, not only in Vietnamese punctuation prediction but also in other related fields. Our project, including datasets and implementation details, is publicly …

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication …

Systematic Classification Of Attackers Via Bounded Model Checking, Eric Rothstein-Morris, Jun Sun, Sudipta Chattopadyay

Research Collection School Of Computing and Information Systems

In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark …

Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang

Research Collection School Of Computing and Information Systems

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have …

Deterministic Identity-Based Encryption From Lattice-Based Programmable Hash Functions With High Min-Entropy, Daode Zhang, Jie Li, Bao Li, Xianhui Lu, Haiyang Xue, Dingding Jia, Yamin Liu

Research Collection School Of Computing and Information Systems

There only exists one deterministic identity-based encryption (DIBE) scheme which is adaptively secure in the auxiliary-input setting, under the learning with errors (LWE) assumption. However, the master public key consists of basic matrices. In this paper, we consider to construct adaptively secure DIBE schemes with more compact public parameters from the LWE problem. (i) On the one hand, we gave a generic DIBE construction from lattice-based programmable hash functions with high min-entropy. (ii) On the other hand, when instantiating our generic DIBE construction with four LPHFs with high min-entropy, we can get four adaptively secure DIBE schemes with more compact …

Food Fraud In Nigeria: Challenges, Risks And Solutions, Joy Ewomazino Opia

Theses

Food fraud is one of the most urgent and active food research and regulatory areas. It is an evolving problem in Nigeria that has led to the deaths of many people especially the vunerable groups that includes mostly children, the elderly and immunocomprised persons. Therefore the aim of this study is to investigate the current challenges of food fraud in Nigeria, identify the risks it poses on the health and wellbeing of Nigerians and propose measures to tackle food fraud at local and international levels by regulatory and government agencies. This study explored the relationship between food fraud, food security …

Usability Of Portable Eeg For Monitoring Students’ Attention In Online Learning, Arisaphat Suttidee

CCAC Theses and Dissertations

Current research demonstrates that distractions while participating in online courses affect students’ performance in online tasks. Electroencephalography (EEG) devices are currently being used in education to help students maintain attention when engaged in online classes. Previous studies have focused predominantly on comparing EEG devices, EEG signal quality, and EEG effectiveness. However, there is no comprehensive study examining the usability of the portable EEG headset to monitor students' attention in online courses.

This study aimed to examine the usability of EEG devices while monitoring student attention levels during online educational tasks. Specifically, twenty (20) participants who intend to enroll in online …

Implementing Algorithmic Crisis Alerts In Mhealth Systems For Veterans With Ptsd, Md Sazzad Hossain, Priyanka Annapureddy, Sheikh Iqbal Ahamed, Praveen Madiraju, Mark Flower, Lisa Rein, Thomas Kissane, Wylie Frydrychowicz, Naveen K. Bansal, Niharika Jain, Katinka Hooyer, Zeno Franco

Computer Science Faculty Research and Publications

This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer …

The Trust Principles For Digital Repositories, Dawei Lin, Jonathan Crabtree, Ingrid Dillo, Robert R. Downs, Rorie Edmunds, David Giaretta, Marisa De Giusti, Hervé L'Hours, Wim Hugo, Reyna Jenkyns, Varsha Khodiyar, Maryann E. Martone, Mustapha Mokrane, Vivek Navale, Jonathan Petters, Barbara Sierman, Dina V. Sokolova, Martina Stockhause, John Westbrook

Copyright, Fair Use, Scholarly Communication, etc.

As information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold.

Following a year-long public discussion and building on existing community consensus , several stakeholders, representing various segments of the digital repository community, have collaboratively developed and endorsed a set of guiding principles to demonstrate digital repository trustworthiness. Transparency, Responsibility, User focus, …

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin

Research Collection School Of Computing and Information Systems

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …

Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text …