no code implementations • 16 Nov 2016 • Jianbo Yuan, Walid Shalaby, Mohammed Korayem, David Lin, Khalifeh Aljadda, Jiebo Luo
One of the most important features of the proposed technique is the fact that it can be applied on top of any existing CF based recommendation engine without changing the CF core.
no code implementations • 20 Sep 2016 • Mohammed Korayem, Khalifeh Aljadda, Trey Grainger
Recommendation emails are among the best ways to re-engage with customers after they have left a website.
5 code implementations • 2 Sep 2016 • Trey Grainger, Khalifeh Aljadda, Mohammed Korayem, Andries Smith
This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph.
no code implementations • 4 Jul 2016 • Shuo Yang, Mohammed Korayem, Khalifeh Aljadda, Trey Grainger, Sriraam Natarajan
In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system.
no code implementations • 1 Jan 2016 • Mohammed Korayem, Khalifeh Aljadda, David Crandall
This paper surveys different ways used for building systems for subjective and sentiment analysis for languages other than English.
no code implementations • 28 Dec 2015 • Khalifeh AlJadda, Rene Ranzinger, Melody Porterfield, Brent Weatherly, Mohammed Korayem, John A. Miller, Khaled Rasheed, Krys J. Kochut, William S. York
The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO).
no code implementations • 28 Dec 2015 • Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger, John A. Miller, Khaled Rasheed, Krys J. Kochut, William S. York, Rene Ranzinger, Melody Porterfield
In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space.
no code implementations • 21 Jul 2014 • Khalifeh AlJadda, Mohammed Korayem, Camilo Ortiz, Trey Grainger, John A. Miller, William S. York
When modeling this kind of hierarchical data across large data sets, Bayesian networks become infeasible for representing the probability distributions for the following reasons: i) Each level represents a single random variable with hundreds of thousands of values, ii) The number of levels is usually small, so there are also few random variables, and iii) The structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes, so the network would contain a single linear path for the random variables from each parent to each child node.