LEARNING-based Focused WEB Crawler

Kumar, Naresh; Aggarwal, Dhruv

doi:10.1080/03772063.2021.1885312

Cited by 16 publications

(13 citation statements)

References 19 publications

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“…These parameters are important to shortlist only the best companies, so that irrelevant options are not displayed to the user. This was done by crawling and scraping the concerned web listings [ 27 , 28 ] for these companies.…”

Section: Proposed Systemmentioning

confidence: 99%

Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.

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Section: Proposed Systemmentioning

confidence: 99%

Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…In respect of recommendations accuracy, the empirical findings suggest that their multidimensional inverse similarity recommendation method (MDITCF) built on time-aware CF surpasses the Inverted CF recommendation method. Authors in [15] suggested a novel deep CF approach to service recommendations, dubbed Location-aware Deep CF (LDCF). These main advancements included in this approach are: 1) the location attributes are located into high-dimensional dense imbed matrices.…”

Section: Literature Surveymentioning

confidence: 99%

“…With this, LDCF not only understands the high-dimension and non-linear correlation among the user and services, but it can also dramatically reduce data sparseness. Extensive studies on a real-world Web service [15] database show that LDCF's recommendations efficiency clearly exceeds 9 state-of-the-art services recommendations approaches. Authors in [16] used location information in the factorization machine for QoS predictions, while contextualized information helps the CF similarity calculation.…”

Section: Literature Surveymentioning

confidence: 99%

An Empirical Framework for Recommendation-based Location Services Using Deep Learning

Rohilla

Kaur

Chakraborty³

2022

Eng. Technol. Appl. Sci. Res.

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The large amount of possible online services throws a significant load on the users' service selection decision-making procedure. Α number of intelligent suggestion systems have been created in order to lower the excessive decision-making expense. Taking this into consideration, aν RLSD (Recommendation-based Location Services using Deep Learning) model is proposed in this paper. Alongside robustness, this research considers the geographic interface between the client and the service. The suggested model blends a Multi-Layer-Perceptron (MLP) with a similarity Adaptive Corrector (AC), which is meant to detect high-dimensional and non-linear connections, as well as the location correlations amongst client and services. This not only improves recommendation results but also considerably reduces difficulties due to data sparseness. As a result, the proposed RLSD has strong flexibility and is extensible when it comes to leveraging context data like location.

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“…With the rapid development of the global network, a large amount of information and data has flooded into the Internet [ 4 ]. Understanding how to effectively acquire useful information from network public data has emerged as a main issue.…”

Section: Introductionmentioning

confidence: 99%

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

Guan

Wang

Yang

et al. 2022

IJERPH

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The openly released and measured data from automatic hydrological and water quality stations in China provide strong data support for water environmental protection management and scientific research. However, current public data on hydrology and water quality only provide real-time data through data tables in a shared page. To excavate the supporting effect of these data on water environmental protection, this paper designs a water-quality-prediction and pollution-risk early-warning system. In this system, crawler technology was used for data collection from public real-time data. Additionally, a modified long short-term memory (LSTM) was adopted to predict the water quality and provide an early warning for pollution risks. According to geographic information technology, this system can show the process of spatial and temporal variations of hydrology and water quality in China. At the same time, the current and future water quality of important monitoring sites can be quickly evaluated and predicted, together with the pollution-risk early warning. The data collected and the water-quality-prediction technique in the system can be shared and used for supporting hydrology and in water quality research and management.

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LEARNING-based Focused WEB Crawler

Cited by 16 publications

References 19 publications

Technical Job Recommendation System Using APIs and Web Crawling

Technical Job Recommendation System Using APIs and Web Crawling

An Empirical Framework for Recommendation-based Location Services Using Deep Learning

Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

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