18 citations · 48 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domain
Trey Grainger, Khalifeh AlJadda, Mohammed Korayem +1
This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverage…
Augmenting recommendation systems using a model of semantically-related terms extracted from user behavior
Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz +5
Common difficulties like the cold-start problem and a lack of sufficient information about users due to their limited interactions have been major challenges for most recommender s…