5 citations · 13 across the 7 of their papers we have counts for
9 papers
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…
Macro-optimization of email recommendation response rates harnessing individual activity levels and group affinity trends
Mohammed Korayem, Khalifeh Aljadda, Trey Grainger
Recommendation emails are among the best ways to re-engage with customers after they have left a website. While on-site recommendation systems focus on finding the most relevant it…
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…
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…
GELATO and SAGE: An Integrated Framework for MS Annotation
Khalifeh AlJadda, Rene Ranzinger, Melody Porterfield +6
Several algorithms and tools have been developed to (semi) automate the process of glycan identification by interpreting Mass Spectrometric data. However, each has limitations when…
Sentiment/Subjectivity Analysis Survey for Languages other than English
Mohammed Korayem, Khalifeh Aljadda, David Crandall
Subjective and sentiment analysis have gained considerable attention recently. Most of the resources and systems built so far are done for English. The need for designing systems f…
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz +7
Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Baye…
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…
PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems
Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz +3
In the big data era, scalability has become a crucial requirement for any useful computational model. Probabilistic graphical models are very useful for mining and discovering data…