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Articles 13441 - 13470 of 63017

Full-Text Articles in Computer Sciences

Why 1/(1+D) Is An Effective Distance-Based Similarity Measure: Two Explanations, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich Oct 2022

Why 1/(1+D) Is An Effective Distance-Based Similarity Measure: Two Explanations, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Most of our decisions are based on the notion of similarity: we use a decision that helped in similar situations. From this viewpoint, it is important to have, for each pair of situations or objects, a numerical value describing similarity between them. This is called a similarity measure. In some cases, the only information that we can use to estimate the similarity value is some natural distance measure d(a,b). In many such situations, empirical data shows that the similarity measure 1/(1+d) is very effective. In this paper, we provide two explanations for this effectiveness.

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How To Describe Variety Of A Probability Distribution: A Possible Answer To Yager's Question, Vladik Kreinovich Oct 2022

How To Describe Variety Of A Probability Distribution: A Possible Answer To Yager's Question, Vladik Kreinovich

Departmental Technical Reports (CS)

Entropy is a natural measure of randomness. It progresses from its smallest possible value 0 -- when we have a deterministic case in which one alternative i occurs with probability 1 (pi = 1), to the largest possible value which is attained at a uniform distribution p1 = ... = pn = 1/n. Intuitively, both in the deterministic case and in the uniform distribution case, there is not much variety in the distribution, while in the intermediate cases, when we have several different values pi, there is a strong variety. Entropy does not seem to capture this notion of variety. …

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The Rock 2022, School Of Engineering And Computer Science Oct 2022

The Rock 2022, School Of Engineering And Computer Science

The Rock

No abstract provided.

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Efficient Data Collection In Iot Networks Using Trajectory Encoded With Geometric Shapes, Xiaofei Cao, Sanjay Kumar Madria Oct 2022

Efficient Data Collection In Iot Networks Using Trajectory Encoded With Geometric Shapes, Xiaofei Cao, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

The mobile edge computing (MEC) paradigm changes the role of edge devices from data producers and requesters to data consumers and processors. MEC mitigates the bandwidth limitation between the edge server and the remote cloud by directly processing the large amount of data locally generated by the network of the internet of things (IoT) at the edge. An efficient data-gathering scheme is crucial for providing quality of service (QoS) within MEC. To reduce redundant data transmission, this paper proposes a data collection scheme that only gathers the necessary data from IoT devices (like wireless sensors) along a trajectory. Instead of …

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How The Pavement Strength Changes With Time: Ai Ideas Help To Explain Semi-Empirical Formulas, Edgar Daniel Rodriguez Velasquez, Vladik Kreinovich Oct 2022

How The Pavement Strength Changes With Time: Ai Ideas Help To Explain Semi-Empirical Formulas, Edgar Daniel Rodriguez Velasquez, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we use AI ideas to provide a theoretical explanation for semi-empirical formulas that describe how the pavement strength changes with time, and how we can predict the pavement lifetime.

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A Simpler Machine Learning Model For Acute Kidney Injury Risk Stratification In Hospitalized Patients, Yirui Hu, Kunpeng Liu, Kevin Ho, David Riviello, Jason Brown, Alex R. Chang, Gurmukteshwar Singh, H. Lester Kirchner Oct 2022

A Simpler Machine Learning Model For Acute Kidney Injury Risk Stratification In Hospitalized Patients, Yirui Hu, Kunpeng Liu, Kevin Ho, David Riviello, Jason Brown, Alex R. Chang, Gurmukteshwar Singh, H. Lester Kirchner

Computer Science Faculty Publications and Presentations

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during …

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Rewards And Challenges In Adopting Agility In An Academic Department, Massood Towhidnejad, Omar Ochoa, James J. Pembridge, Radu Babiceanu, Carlos Castro Oct 2022

Rewards And Challenges In Adopting Agility In An Academic Department, Massood Towhidnejad, Omar Ochoa, James J. Pembridge, Radu Babiceanu, Carlos Castro

Posters

Introducing agility into department processes may be challenging especially when interfacing with a non-agile environment. While frequent meetings can add more time constraints, the team environment emphasizes more communication, transparency, and accountability in completing the products leading to a higher sense of ownership of the completed work.

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An Energy Efficient Smart Metering System Using Edge Computing In Lora Network, Preti Kumari, Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das Oct 2022

An Energy Efficient Smart Metering System Using Edge Computing In Lora Network, Preti Kumari, Rahul Mishra, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das

Computer Science Faculty Research & Creative Works

An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a …

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Combining Security And Reliability Of Critical Infrastructures: The Concept Of Securability, Leandros Maglaras, Helge Janicke, Mohamed Amine Ferrag Oct 2022

Combining Security And Reliability Of Critical Infrastructures: The Concept Of Securability, Leandros Maglaras, Helge Janicke, Mohamed Amine Ferrag

Research outputs 2022 to 2026

The digital revolution has made people more dependent on ICT technology to perform everyday tasks, whether at home or at work. The systems that support critical aspects of this smart way of living are characterized as critical, and the security level of such systems is higher as compared to others. The definition of the criticality of a system is a rather difficult exercise, and for that reason, we have seen novel cybersecurity regulations to introduce the idea of digital managed services, which include security monitoring, managed network services, or the outsourcing of business processes that are are critical to the …

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Physical Adversarial Attack On A Robotic Arm, Yifan Jia, Christopher M. Poskitt, Jun Sun, Sudipta Chattopadhyay Oct 2022

Physical Adversarial Attack On A Robotic Arm, Yifan Jia, Christopher M. Poskitt, Jun Sun, Sudipta Chattopadhyay

Research Collection School Of Computing and Information Systems

Collaborative Robots (cobots) are regarded as highly safety-critical cyber-physical systems (CPSs) owing to their close physical interactions with humans. In settings such as smart factories, they are frequently augmented with AI. For example, in order to move materials, cobots utilize object detectors based on deep learning models. Deep learning, however, has been demonstrated as vulnerable to adversarial attacks: a minor change (noise) to benign input can fool the underlying neural networks and lead to a different result. While existing works have explored such attacks in the context of picture/object classification, less attention has been given to attacking neural networks used …

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What Machines Can't Do (Yet) In Real Work Settings, Thomas H. Davenport, Steven M. Miller Oct 2022

What Machines Can't Do (Yet) In Real Work Settings, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

AI systems may perform well in the research lab or under highly controlled application settings, but they still needed human help in the types of real world work settings we researched for a new book, Working With AI: Real Stories of Human-Machine Collaboration. Human workers were very much in evidence across our 30 case studies. In this article, we use those examples to illustrate our list of AI-enabled activities that still require human assistance. These are activities where organizations need to continue to invest in human capital, and where practitioners can expect job continuity for the immediate future

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Tgdm: Target Guided Dynamic Mixup For Cross-Domain Few-Shot Learning, Linhai Zhuo, Yuqian Fu, Jingjing Chen, Yixin Cao, Yu-Gang Jiang Oct 2022

Tgdm: Target Guided Dynamic Mixup For Cross-Domain Few-Shot Learning, Linhai Zhuo, Yuqian Fu, Jingjing Chen, Yixin Cao, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the target domain. To help knowledge transfer, this paper introduces an intermediate domain generated by mixing images in the source and the target domain. Specifically, to generate the optimal intermediate domain for different target data, we propose a novel target guided dynamic mixup (TGDM) framework that leverages the target …

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Long-Term Leap Attention, Short-Term Periodic Shift For Video Classification, Hao Zhang, Lechao Cheng, Yanbin Hao, Chong-Wah Ngo Oct 2022

Long-Term Leap Attention, Short-Term Periodic Shift For Video Classification, Hao Zhang, Lechao Cheng, Yanbin Hao, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes �� times longer sequence than the latter under the current attention of quadratic complexity (�� 2�� 2 ). The existing works treat the temporal axis as a simple extension of spatial axes, focusing on shortening the spatio-temporal sequence by either generic pooling or local windowing without utilizing temporal redundancy. However, videos naturally contain redundant information between neighboring frames; thereby, we could potentially suppress attention on visually similar frames in a dilated manner. Based on this hypothesis, we propose the LAPS, a long-term “Leap …

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Autoprtitle: A Tool For Automatic Pull Request Title Generation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, David Lo, Lingxiao Jiang Oct 2022

Autoprtitle: A Tool For Automatic Pull Request Title Generation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

With the rise of the pull request mechanism in software development, the quality of pull requests has gained more attention. Prior works focus on improving the quality of pull request descriptions and several approaches have been proposed to automatically generate pull request descriptions. As an essential component of a pull request, pull request titles have not received a similar level of attention. To further facilitate automation in software development and to help developers draft high-quality pull request titles, we introduce AutoPRTitle. AutoPRTitle is specifically designed to generate pull request titles automatically. AutoPRTitle can generate a precise and succinct pull request …

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Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Dulanga Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra Oct 2022

Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Dulanga Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra

Research Collection School Of Computing and Information Systems

Supporting real-time referring expression comprehension (REC) on pervasive devices is an important capability for human-AI collaborative tasks. Model pruning techniques, applied to DNN models, can enable real-time execution even on resource-constrained devices. However, existing pruning strategies are designed principally for uni-modal applications, and suffer a significant loss of accuracy when applied to REC tasks that require fusion of textual and visual inputs. We thus present a multi-modal pruning model, LGMDP, which uses language as a pivot to dynamically and judiciously select the relevant computational blocks that need to be executed. LGMDP also introduces a new SoftSkip mechanism, whereby 'skipped' visual …

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Improving Knowledge-Aware Recommendation With Multi-Level Interactive Contrastive Learning, Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, Dangyang Chen Oct 2022

Improving Knowledge-Aware Recommendation With Multi-Level Interactive Contrastive Learning, Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, Dangyang Chen

Research Collection School Of Computing and Information Systems

Incorporating Knowledge Graphs (KG) into recommeder system as side information has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, from the following aspects: 1) the sparse interaction, itself, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) further results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring …

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Exploring The Impact Of Gender Bias Mitigation Approaches On A Downstream Classification Task, Nasim Sobhani, Sarah Jane Delany Oct 2022

Exploring The Impact Of Gender Bias Mitigation Approaches On A Downstream Classification Task, Nasim Sobhani, Sarah Jane Delany

Conference Papers

Natural language models and systems have been shown to reflect gender bias existing in training data. This bias can impact on the downstream task that machine learning models, built on this training data, are to accomplish. A variety of techniques have been proposed to mitigate gender bias in training data. In this paper we compare different gender bias mitigation approaches on a classification task. We consider mitigation techniques that manipulate the training data itself, including data scrubbing, gender swapping and counterfactual data augmentation approaches. We also look at using de-biased word embeddings in the representation of the training data. We …

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Essm: An Extractive Summarization Model With Enhanced Spatial-Temporal Information And Span Mask Encoding, Ran Li, Fengbo Zheng, Gongbo Liang, Lifen Jiang, Panpan Wu, Bowei Chen Oct 2022

Essm: An Extractive Summarization Model With Enhanced Spatial-Temporal Information And Span Mask Encoding, Ran Li, Fengbo Zheng, Gongbo Liang, Lifen Jiang, Panpan Wu, Bowei Chen

Computer Science Faculty Publications (Archived)

Extractive reading comprehension is to extract consecutive subsequences from a given article to answer the given question. Previous work often adopted Byte Pair Encoding (BPE) that could cause semantically correlated words to be separated. Also, previous features extraction strategy cannot effectively capture the global semantic information. In this paper, an extractive summarization model is proposed with enhanced spatial-temporal information and span mask encoding (ESSM) to promote global semantic information. ESSM utilizes Embedding Layer to reduce semantic segmentation of correlated words, and adopts TemporalConvNet Layer to relief the loss of feature information. The model can also deal with unanswerable questions. To …

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Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen Oct 2022

Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen

Doctoral Dissertations and Master's Theses

Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to …

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Multi-Bsm: An Anomaly Detection And Position Falsification Attack Mitigation Approach In Connected Vehicles, Zouheir Trabelsi, Syed Sarmad Shah, Kadhim Hayawi Oct 2022

Multi-Bsm: An Anomaly Detection And Position Falsification Attack Mitigation Approach In Connected Vehicles, Zouheir Trabelsi, Syed Sarmad Shah, Kadhim Hayawi

All Works

With the dawn of the emerging technologies in the field of vehicular environment, connected vehicles are advancing at a rapid speed. The advancement of such technologies helps people daily, whether it is to reach from one place to another, avoid traffic, or prevent any hazardous incident from occurring. Safety is one of the main concerns regarding the vehicular environment when it comes to developing applications for connected vehicles. Connected vehicles depend on messages known as basic safety messages (BSMs) that are repeatedly broadcast in their communication range in order to obtain information regarding their surroundings. Different kinds of attacks can …

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Pandemic Time And Tourism In Oecd Countries: Artificial Intelligence And Digital Platforms, Alfonso Marino, Paolo Pariso, Michele Picariello Oct 2022

Pandemic Time And Tourism In Oecd Countries: Artificial Intelligence And Digital Platforms, Alfonso Marino, Paolo Pariso, Michele Picariello

University of South Florida (USF) M3 Publishing

Introduction underline the three phases related to sector crisis, Background, starting from literature highlight the importance of what are the main actions implemented in 38 Member States. Methodology, with SPAD, elaborates a qualitative and quantitative set of policy responses that are displayed in Results. Discussions highlight the different approaches within the OECD area, but also the absence of a common strategy to exit to the sector crisis. The conclusion emphasizes that crisis response policies still need to be built and developed in the OECD area, even though initial responses showed strong responses in individual Member States that did not address …

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(Si10-124) Inverse Reconstruction Methodologies: A Review, Deepika Saini Oct 2022

(Si10-124) Inverse Reconstruction Methodologies: A Review, Deepika Saini

Applications and Applied Mathematics: An International Journal (AAM)

The three-dimensional reconstruction problem is a longstanding ill-posed problem, which has made enormous progress in the field of computer vision. This field has attracted increasing interest and demonstrated an impressive performance. Due to a long era of increasing evolution, this paper presents an extensive review of the developments made in this field. For the three dimensional visualization, researchers have focused on the developments of three dimensional information and acquisition methodologies from two dimensional scenes or objects. These acquisition methodologies require a complex calibration procedure which is not practical in general. Hence, the requirement of flexibility was much needed in all …

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Hierarchical Hourglass Convolutional Network For Efficient Video Classification, Yi Tan, Yanbin Hao, Hao Zhang, Shuo Wang Oct 2022

Hierarchical Hourglass Convolutional Network For Efficient Video Classification, Yi Tan, Yanbin Hao, Hao Zhang, Shuo Wang

PhD Student’s Publications Collection

Videos naturally contain dynamic variation over the temporal axis, which will result in the same visual clues (e.g., semantics, objects) changing their scale, position, and perspective patterns between adjacent frames. A primary trend in video CNN is adopting spatial-2D convolution for spatial semantics and temporal-1D convolution for temporal dynamics. Though the direction achieves a favorable balance between efficiency and efficacy, it suffers from misalignment of visual clues with large displacements. Particularly, rigid temporal convolution would fail to capture correct motions when a specific target moves out of the reception field of temporal convolution between adjacent frames.To tackle large visual displacements …

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Soci: A Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Jiaming Yuan, Ximeng Liu, Yongdong Wu, Hwee Hwa Pang, Robert H. Deng Oct 2022

Soci: A Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Jiaming Yuan, Ximeng Liu, Yongdong Wu, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Secure outsourced computation is a key technique for protecting data security and privacy in the cloud. Although fully homomorphic encryption (FHE) enables computations over encrypted data, it suffers from high computation costs in order to support an unlimited number of arithmetic operations. Recently, secure computations based on interactions of multiple computation servers and partially homomorphic encryption (PHE) were proposed in the literature, which enable an unbound number of addition and multiplication operations on encrypted data more efficiently than FHE and do not add any noise to encrypted data; however, these existing solutions are either limited in functionalities (e.g., computation on …

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Gray Counters For Non-Volatile Memories, Arockia David Roy Kulandai, John Rose, Thomas Schwarz Oct 2022

Gray Counters For Non-Volatile Memories, Arockia David Roy Kulandai, John Rose, Thomas Schwarz

Computer Science Faculty Research and Publications

New technologies for non-volatile memories combine the speed and byte addressability of current memory technologies with the low cost, density, and non-volatility of current storage technologies. They use energy only when writing or reading data. While some newer technologies have practically unlimited endurance, others, such as Phase Change Memory do not. However, this limited endurance surpasses that of solid state drives by several orders of magnitude. They can be integrated into the current memory storage hierarchy as a replacement for DRAM. To manage limited endurance, age-based wear leveling divides the memory into pages and counts the number of writes to …

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Sustaining Patient Portal Continuous Use Intention And Enhancing Deep Structure Usage: Cognitive Dissonance Effects Of Health Professional Encouragement And Security Concerns, Murad Moqbel, Barbara Hewitt, Fiona Fui-Hoon Nah, Rosann M. Mclean Oct 2022

Sustaining Patient Portal Continuous Use Intention And Enhancing Deep Structure Usage: Cognitive Dissonance Effects Of Health Professional Encouragement And Security Concerns, Murad Moqbel, Barbara Hewitt, Fiona Fui-Hoon Nah, Rosann M. Mclean

Research Collection School Of Computing and Information Systems

Sustaining patient portal use is a major problem for many healthcare organizations and providers. If this problem can be successfully addressed, it could have a positive impact on various stakeholders. Through the lens of cognitive dissonance theory, this study investigates the role of health professional encouragement as well as patients’ security concerns in influencing continuous use intention and deep structure usage among users of a patient portal. The analysis of data collected from 177 patients at a major medical center in the Midwestern region of the United States shows that health professional encouragement helps increase the continuous use intention and …

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Towards Robust Models Of Code Via Energy-Based Learning On Auxiliary Datasets, Duy Quoc Nghi Bui, Yijun Yu Oct 2022

Towards Robust Models Of Code Via Energy-Based Learning On Auxiliary Datasets, Duy Quoc Nghi Bui, Yijun Yu

Research Collection School Of Computing and Information Systems

Existing approaches to improving the robustness of source code models concentrate on recognizing adversarial samples rather than valid samples that fall outside of a given distribution, which we refer to as out-of-distribution (OOD) samples. To this end, we propose to use an auxiliary dataset (out-of-distribution) such that, when trained together with the main dataset, they will enhance the model’s robustness. We adapt energy-bounded learning objective function to assign a higher score to in-distribution samples and a lower score to out-of-distribution samples in order to incorporate such out-of-distribution samples into the training process of source code models. In terms of OOD …

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A Comparative Analysis Of Clone Detection Techniques On Semanticclonebench, Sohaib Masood Rabbani, Nabeel Ahmad Gulzar, Saad Arshad, Shamsa Abid, Shafay Shamail Oct 2022

A Comparative Analysis Of Clone Detection Techniques On Semanticclonebench, Sohaib Masood Rabbani, Nabeel Ahmad Gulzar, Saad Arshad, Shamsa Abid, Shafay Shamail

Research Collection School Of Computing and Information Systems

Semantic code clone detection involves the detection of functionally similar code fragments which may otherwise be lexically, syntactically, or structurally dissimilar. The detection of semantic code clones has important applications in aspect mining and product line analysis. The accurate detection of semantic code clones is a challenging task and various techniques have been proposed. However, the evaluation of these techniques is performed using various datasets and we do not have a clear picture of the performance of these techniques relative to each other. Recently, SemanticCloneBench has been introduced as a benchmark for semantic clones. Now, we can use the SemanticCloneBench …

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Video Graph Transformer For Video Question Answering, Junbin Xiao, Pan Zhou, Tat-Seng Chua, Shuicheng Yan Oct 2022

Video Graph Transformer For Video Question Answering, Junbin Xiao, Pan Zhou, Tat-Seng Chua, Shuicheng Yan

Research Collection School Of Computing and Information Systems

This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT’s uniqueness are two-fold: 1) it designs a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations, and dynamics for complex spatio-temporal reasoning; and 2) it exploits disentangled video and text Transformers for relevance comparison between the video and text to perform QA, instead of entangled crossmodal Transformer for answer classification. Vision-text communication is done by additional cross-modal interaction modules. With more reasonable video encoding and QA solution, we show that VGT can achieve much better performances on VideoQA tasks …

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Toward Personalized Answer Generation In E-Commerce Via Multi-Perspective Preference Modeling, Yang Deng, Yaliang Li, Wenxuan Zhang, Bolin Ding, Wai Lam Oct 2022

Toward Personalized Answer Generation In E-Commerce Via Multi-Perspective Preference Modeling, Yang Deng, Yaliang Li, Wenxuan Zhang, Bolin Ding, Wai Lam

Research Collection School Of Computing and Information Systems

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generation for product-related questions, has been studied by aiming to generate content-preserving while question-related answers. However, an important characteristic of PQA, i.e., personalization, is neglected by existing methods. It is insufficient to provide the same “completely summarized” answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves, by taking into consideration their own preferences toward product aspects …

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