13 citations · 23 across the 6 of their papers we have counts for
6 papers
Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach
Jianbo Yuan, Walid Shalaby, Mohammed Korayem +3
Collaborative Filtering (CF) is widely used in large-scale recommendation engines because of its efficiency, accuracy and scalability. However, in practice, the fact that recommend…
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…
ScreenAvoider: Protecting Computer Screens from Ubiquitous Cameras
Mohammed Korayem, Robert Templeman, Dennis Chen +2
We live and work in environments that are inundated with cameras embedded in devices such as phones, tablets, laptops, and monitors. Newer wearable devices like Google Glass, Narra…
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…