Machine learning-based product recommendation using Apache Spark

Chen, Lin; Li, Rui; Liu, Yige; Zhang, Ruixuan; Woodbridge, Diane

doi:10.1109/uic-atc.2017.8397470

Cited by 14 publications

(10 citation statements)

References 6 publications

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“…This section provides a comprehensive comparison with recent studies 30‐42 related to our approach. This comparison considers the underlying technology, including open data, machine learning, semantic web, and cloud computing, as shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…The GoDaaS still lacks intelligence, such as machine learning or semantic web technologies, to process the OGD. Several studies 32‐34,36,41 have focused on using machine learning technologies, including supervised learning, unsupervised learning, bayesian network, to combine OD to develop an intelligent application, such as recommender system and decision system. By contrast, some studies 30,32,34,35,38,41,42 focus on adopting semantic web technologies, including RDF, RDF schema, OWL, associated with OD to provide specific domain knowledge and promote intelligence of application.…”

Section: Related Workmentioning

confidence: 99%

“…By contrast, some studies 30,32,34,35,38,41,42 focus on adopting semantic web technologies, including RDF, RDF schema, OWL, associated with OD to provide specific domain knowledge and promote intelligence of application. Other significant researches 31‐33,37,39,40 try to use cloud computing, such as Spark, Hadoop, and edge computing, to improve performance. None of the aforementioned studies can integrate machine learning, semantic web, and cloud computing in OD applications.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu

Lin

2020

Softw Pract Exp

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Summary Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Hsu

Lin

2020

Softw Pract Exp

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“…Apart from these, online product configuration, identification, and recommendation system using advanced techniques/methods are presented in [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] (Table 4). Apart from this, [53][54][55], they represent the recommendation system based on personal behavior, ML, and survey respectively. However, most of the above-mentioned techniques are very complex, not too easy to understand, and operation by the operator/researchers, time-consuming and required a huge volume of data storage.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Targeting and Recommending a Product for Project Management

et al. 2021

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Conventionally, a market research and strategy for a product depends on the interviews and an explicit cluster/society to identify the customer’s needs. Customer-created information (CCI), such as call-center data, online reviews, and social media posts, provides an opportunity to recognize the customer’s needs more efficiently. Moreover, developed conventional approaches are not compatible with large CCI datasets because most of the CCI-contents are repetitive and uninformative. In this paper, a machine learning approach for identifying the customer needs from the CCI dataset is proposed and its performance is evaluated for targeting and recommending a new product for project management. After the identification of the needs of the customer, information can be used to develop a market strategy, new product launching, brand positioning and much more long/short term planning.

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“…For more than a decade, ML has been a powerful tool applied to all scientific fields, including speech recognition [14], translation between languages [17], emotion recognition [18], autonomous navigation of vehicles [19], product recommendations [20] and image processing [7,21]. Notably, there is a fast-growing trend of using ML algorithms in the health care industry [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

2020

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Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that is increasingly applied to several medical diagnosis tasks, including a wide range of diseases. Importantly, various ML models were developed to address the complexity of Parkinson's Disease (PD) diagnosis. PD is a neurodegenerative disease characterized by motor and non-motor disorders where its syndromes affect the daily lives of patients. Several Computer Aided Diagnosis and Detection (CADD) systems based on hand-crafted ML algorithms achieved promising results in distinguishing PD patients from Healthy Control (HC) subjects and other Parkinsonian syndrome categories using clinical data (e.g., speech and gait impairments) and medical imaging [e.g., Position Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)]. Despite the good performance of hand-crafted ML algorithms, there is still a problem linked to the features' extraction and selection. In fact, Deep Learning DL has provided an ultimate solution for the features' extraction and selection related issue. An important number of studies on the diagnosis of PD using DL algorithms were developed recently. This study provides an overview of the application of hand-crafted ML algorithms and DL techniques for PD diagnosis. It also introduces key concepts for understanding the application of ML methods to diagnose PD.

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Machine learning-based product recommendation using Apache Spark

Cited by 14 publications

References 6 publications

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Machine Learning Approach for Targeting and Recommending a Product for Project Management

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

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