Efficient and Scalable Job Recommender System Using Collaborative Filtering

Mishra, Ravita; Rathi, Sheetal

doi:10.1007/978-981-15-1420-3_91

Cited by 27 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hybrid filtering memadukan content dan collaborative filtering, meningkatkan akurasi dan memberikan hasil terbaik dengan mengeksplorasi penerapan sistem rekomendasi pada perekrutan pekerja, mengatasi kelemahan pada sistem yang sudah ada. [5]. Model konseptual dan mengeksplorasi berbagai algoritma CF dalam konteks rekomendasi destinasi wisata, menunjukkan bahwa algoritma Bilateral Variational Autoencoder (BiVAE) unggul dalam kinerja [6].…”

Section: Iunclassified

Pengelolaan Populasi Kucing Liar Melalui Collaborative Filtering: Dampak Terhadap Kesehatan Masyarakat Dan Ekosistem Lokal

Hidayat,

Supriyono

2024

INDEXIA

View full text Add to dashboard Cite

Seiring berjalannya waktu, kebersamaan sahabat kucing telah menjadi aspek yang abadi dalam kehidupan kita. Namun, sebuah masalah signifikan telah muncul: reproduksi kucing yang cepat, mengakibatkan kelebihan populasi yang tidak terkendali. Kelebihan hewan peliharaan ini memiliki dampak yang besar pada masyarakat sekitar. Banyaknya kucing liar menimbulkan tantangan besar, termasuk meningkatnya risiko kesehatan masyarakat akibat penularan penyakit seperti rabies dan toksoplasmosis, gangguan pada ekosistem lokal akibat pemangsaan yang tidak terkendali terhadap burung dan satwa liar kecil, serta masalah kebersihan dan kenyamanan di masyarakat. Lebih jauh lagi, situasi ini merenggangkan hubungan sosial di antara para tetangga, sehingga menimbulkan konflik dan ketidaknyamanan. Untuk mengatasi masalah yang memiliki banyak sisi ini, penelitian ini memperkenalkan pendekatan inovatif kecerdasan buatan yang berpusat pada model Penyaringan Kolaboratif. Sistem ini menggunakan teknik pembelajaran mesin, termasuk Penyaringan Kolaboratif, untuk meningkatkan fungsinya. Menyerupai platform pencocokan yang populer, sistem yang dihasilkan berkolaborasi dengan para profesional kedokteran hewan lokal dan organisasi penyelamatan hewan. Tujuan utamanya adalah untuk memfasilitasi identifikasi pemilik yang bertanggung jawab atas kucing, memastikan kehidupan yang bermartabat bagi mereka. Pendekatan perintis ini menjanjikan dampak positif pada populasi kucing dan masyarakat, serta mendorong koeksistensi yang harmonis. Sifat kolaboratif dari sistem ini, yang melibatkan Rumah Steril dan Lets Adopt Indonesia, menggarisbawahi potensinya untuk mengurangi tantangan yang ditimbulkan oleh kelebihan populasi kucing liar dan mempromosikan hubungan yang seimbang dan saling menghormati antara manusia dan kucing. Hasil Penelitian ini menghasilkan rekomendasi id kucing paling baik: 63, 58, 56, 22, 64, 35, 60, 39, 21, dan 7.

show abstract

Section: Iunclassified

Pengelolaan Populasi Kucing Liar Melalui Collaborative Filtering: Dampak Terhadap Kesehatan Masyarakat Dan Ekosistem Lokal

Hidayat,

Supriyono

2024

INDEXIA

View full text Add to dashboard Cite

show abstract

“…In [3], Ravita Mishra and Sheetal Rathi propose a survey of recommendation system models used in important recruitment platform like LinkedIn.…”

Section: State Of the Artmentioning

confidence: 99%

A Scalable Recommendation System Approach for a Companies — Seniors Matching

Guyard

Deriaz

2023

Int. J. Semantic Computing

View full text Add to dashboard Cite

Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid Content-Based Filtering (CBF) and Similarity Index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.

show abstract

“…However, they use third-party aggregators to fetch the jobs and it is well known that these existing aggregators are not always updated. They cannot fetch jobs directly from the company portals [ 5 ]. Mhamdi et al have designed/devised a job recommendation product that aims to extract meaningful data from job postings on portals.…”

Section: Related Workmentioning

confidence: 99%

“…On evaluating both of these methods, it was concluded that a hybrid system of both of these overcomes the limitations of both of them and increases the efficiency of ranking. Problems of cold start, sparse database, scalability, and lack of trend recommendation [ 5 ] have been eliminated. The proposal is to design a Job Recommender system that prioritizes quality over quantity.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

See 1 more Smart Citation

Technical Job Recommendation System Using APIs and Web Crawling

Kumar¹,

Gupta²,

Sharma

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

Efficient and Scalable Job Recommender System Using Collaborative Filtering

Cited by 27 publications

References 27 publications

Pengelolaan Populasi Kucing Liar Melalui Collaborative Filtering: Dampak Terhadap Kesehatan Masyarakat Dan Ekosistem Lokal

Pengelolaan Populasi Kucing Liar Melalui Collaborative Filtering: Dampak Terhadap Kesehatan Masyarakat Dan Ekosistem Lokal

A Scalable Recommendation System Approach for a Companies — Seniors Matching

Technical Job Recommendation System Using APIs and Web Crawling

Contact Info

Product

Resources

About