Web-scale multi-task feature selection for behavioral targeting
By Amr Ahmed, Mohamed Aly, Abhimanyu Das, Alex Smola, Tasos Anastasakos. 2012
11 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Networks and Communications, Management Science and Operations Research
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In Computational Mechanics
In Computer Networks and Communications
In Management Science and Operations Research
Citations in the corpus
- An Overview of Multi-task Learning (2018, neutral): “Web applications based on MTL include learning to rank in web searches [121], web search ranking [122], multi-domain collaborative filtering [123], behavioral targeting [124], and conversion maximization in display advertising [125].”
- A Survey on Multi-Task Learning (2017, neutral): “In [186], the multi-task feature selection (i.”
- Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising (2014, methodology): “To obtain good performance, publishers tend to instrument their websites with embedded code which allows third parties to capture user transactions and generate user segments that are of high value for a particular advertiser [1].”
- A Split-and-Conquer Approach for Analysis of Extraordinarily Large Data (2014, background): “Most of the the research in computer sciences focuses on the aspects such as accessing to a shared memory, exchanging information between processors, or identifying parallel components within an algorithm, etc.; See, e.g. Ahmed et al. (2012).”