5 citations · 11 across the 6 of their papers we have counts for
7 papers
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…
Entity Type Recognition using an Ensemble of Distributional Semantic Models to Enhance Query Understanding
Walid Shalaby, Khalifeh Al Jadda, Mohammed Korayem +1
We present an ensemble approach for categorizing search query entities in the recruitment domain. Understanding the types of entities expressed in a search query (Company, Skill, J…
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…