activity
20162021
most citedEmbedding-based Recommender System for Job to Candidate Matching on Scale

18 citations · 24 across the 3 of their papers we have counts for

collaborators

5 papers

cs.IR202118 cited

Embedding-based Recommender System for Job to Candidate Matching on Scale

Jing Zhao, Jingya Wang, Madhav Sigdel +4

The online recruitment matching system has been the core technology and service platform in CareerBuilder. One of the major challenges in an online recruitment scenario is to provi…

cs.IR2019

Tripartite Vector Representations for Better Job Recommendation

Mengshu Liu, Jingya Wang, Kareem Abdelfatah +1

Job recommendation is a crucial part of the online job recruitment business. To match the right person with the right job, a good representation of job postings is required. Such r…

cs.LG2019

Automated Discovery and Classification of Training Videos for Career Progression

Alan Chern, Phuong Hoang, Madhav Sigdel +2

Job transitions and upskilling are common actions taken by many industry working professionals throughout their career. With the current rapidly changing job landscape where requir…

cs.IR20182 cited

Help Me Find a Job: A Graph-based Approach for Job Recommendation at Scale

Walid Shalaby, BahaaEddin AlAila, Mohammed Korayem +4

Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings everyday, the need for accurate, effec…

cs.AI20164 cited

Application of Statistical Relational Learning to Hybrid Recommendation Systems

Shuo Yang, Mohammed Korayem, Khalifeh AlJadda +2

Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who i…