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- research-articleAugust 2021
Interactive Audience Expansion On Large Scale Online Visitor Data
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 2621–2631https://doi.org/10.1145/3447548.3467179Online marketing platforms often store millions of website visitors' behavior as a large sparse matrix with rows as visitors and columns as behavior. These platforms allow marketers to conduct Audience Expansion, a technique to identify new audiences ...
- research-articleNovember 2019
Adversarial Factorization Autoencoder for Look-alike Modeling
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge ManagementPages 2803–2812https://doi.org/10.1145/3357384.3357807Digital advertising is performed in multiple ways, for e.g., contextual, display-based and search-based advertising. Across these avenues, the primary goal of the advertiser is to maximize the return on investment. To realize this, the advertiser often ...
- posterMarch 2011
A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns
- Ashish Mangalampalli,
- Adwait Ratnaparkhi,
- Andrew O. Hatch,
- Abraham Bagherjeiran,
- Rajesh Parekh,
- Vikram Pudi
WWW '11: Proceedings of the 20th international conference companion on World wide webPages 85–86https://doi.org/10.1145/1963192.1963236Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been further complemented by recent work in Look-alike Modeling (LAM) which ...
