Asif Islam
Asif's research interests include large-scale machine learning, data/web mining, user modeling, social networks and content analysis. He received his PhD in Computer Science from Stony Brook University, and bachelors in Computer Science & Engineering from Bangladesh University of Engineering and Technology.
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Micro-Browsing Models for Search Snippets
International Conference on Data Engineering (ICDE), IEEE (2019), pp. 1904-1909
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Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR is the product of the probability of examination times the perceived relevance of the result. Hence there has been considerable work on user browsing models to separate out the examination and relevance components of CTR. However, the snippet text often plays a critical role in the perceived relevance of the result. In this paper, we propose a micro-browsing model for how users read result snippets. We validate the user model by considering the problem of predicting which of two different snippets will yield higher CTR. We show that classification accuracy is dramatically higher with our user model.
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