A Job Recommender System Based on User Clustering

Abstract

In this paper, we first provide a comprehensive investigation of four online job recommender systems (JRSs) from four different aspects: user profiling, recommendation strategies, recommendation output, and user feedback. In particular, we summarize the pros and cons of these online JRSs and highlight their differences. We then discuss the challenges in building high-quality JRSs. One main challenge lies on the design of recommendation strategies since different job applicants may have different characteristics. To address the aforementioned challenge, we develop an online JRS, iHR, which groups users into different clusters and employs different recommendation approaches for different user clusters. As a result, iHR has the capability of choosing the appropriate recommendation approaches according to users' characteristics. Empirical results demonstrate the effectiveness of the proposed system.

Job Seekers' Acceptance of Job Recommender Systems: Results of an Empirical Study

“…For both types of recommender systems several content-, collaborative-, knowledge-based, and hybrid recommendation algorithms have been investigated [14]. The majority of the literature investigates matching algorithms that address the bilaterally of the recommendation [12,13,15], the challenge of the various user characteristics that can be used to match job seekers with jobs [15][16][17], and the consideration of social networks for the matching process [13,15,18]. Hence, none of these approaches deals with the acceptance of job recommender systems by job seekers.…”

Career and Skills Recommendations using Data Mining Technique: Matching Right People for Right Profession, in Pakistani Context

“…We also built a user profile which shows the type of item that user likes [5]. According to previous work [6], a job recommender system comprises of a jobseeker or candidate subsystem that is intended for job applicants and an e-recruiting subsystem. Apart from the old fashioned or traditional recommender systems, which only focus on the one-sided preference like the preference of a user on the items, this system is developed on user clustering which generates recommendations separately for each group.…”

Analisis dan Implementasi pendekatan Hybrid untuk Sistem Rekomendasi Pekerjaan dengan Metode Knowledge Based dan Collaborative Filtering

“…Pada penelitian ini, penulis menggunakan dataset dari website Xing untuk merekomendasikan pekerjaan pada pengguna. Xing adalah social network untuk bisnis yang digunakan oleh para pencari kerja dan perekrut untuk menemukan kandidat yang cocok pada pekerjaan yang ditawarkan [3]. Penulis menggabungkan metode knowledge based dan collaborative filtering agar pengguna dapat diberikan rekomendasi berdasarkan karakteristik pekerjaan dari preferensi pengguna, setelah itu diberikan rekomendasi dari social network menggunakan collaborative filtering.…”

“…Penulis menggabungkan metode knowledge based dan collaborative filtering agar pengguna dapat diberikan rekomendasi berdasarkan karakteristik pekerjaan dari preferensi pengguna, setelah itu diberikan rekomendasi dari social network menggunakan collaborative filtering. Rekomendasi hybrid menggabungkan dua metode ini dengan menggunakan social aperture sebagai parameter pengaruh prediksi pada knowledge based recommender system dan collaborative filtering [3]. Tujuan dari pelaksanaan penelitian ini adalah menganalisa pengaruh sistem rekomendasi hybrid berdasarkan hasil prediksi dan rekomendasi dibandingkan dengan metode collaborative filtering dan knowledge based saja.…”