Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Grainger, Trey"'
Publikováno v:
The 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16) , 2016
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 items for a user at the moment (right item), email recommendations add t
Externí odkaz:
http://arxiv.org/abs/1609.05989
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 leverages an inverted index, along with a complementary uninverted index, to r
Externí odkaz:
http://arxiv.org/abs/1609.00464
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 interacted with or rated the target item (collaborative filtering). To
Externí odkaz:
http://arxiv.org/abs/1607.01050
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, Job Title, etc.) enables more intelligent information retrieval based u
Externí odkaz:
http://arxiv.org/abs/1604.00933
Autor:
AlJadda, Khalifeh, Korayem, Mohammed, Ortiz, Camilo, Grainger, Trey, Miller, John A., Rasheed, Khaled, Kochut, Krys J., York, William S., Ranzinger, Rene, Porterfield, Melody
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 Bayesian Network, which is one of the most common PGMs used in machine lea
Externí odkaz:
http://arxiv.org/abs/1512.08525
Autor:
AlJadda, Khalifeh, Korayem, Mohammed, Ortiz, Camilo, Russell, Chris, Bernal, David, Payson, Lamar, Brown, Scott, Grainger, Trey
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 systems (RS). To overcome these challenges and many similar ones that r
Externí odkaz:
http://arxiv.org/abs/1409.2530
Autor:
AlJadda, Khalifeh, Korayem, Mohammed, Ortiz, Camilo, Grainger, Trey, Miller, John A., York, William S.
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 insights, but they are not scalable enough to be suitable for big dat
Externí odkaz:
http://arxiv.org/abs/1407.5656
Autor:
AlJadda, Khalifeh, Korayem, Mohammed, Ortiz, Camilo, Grainger, Trey, Miller, John A., Rasheed, Khaled M., Kochut, Krys J., Peng, Hao, York, William S., Ranzinger, Rene, Porterfield, Melody
Publikováno v:
In Information Sciences January 2018 425:62-75
Akademický článek
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Publikováno v:
2015 IEEE International Conference on Big Data (Big Data); 2015, p1230-1237, 8p