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Logistic Regression with Stochastic Gradient Ascent to Estimate Click Through Rate

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Information and Communication Technology for Sustainable Development

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 9))

Abstract

Majority of Web users utilize search engines to locate Web site links. Based upon the search queries provided by the users, search engines display sponsored advertisements together with actual Web site link results to procreate monetary benefits. However, users may click the concerned sponsored advertisements that generate revenue for the search engines based upon a predefined pricing model. Furthermore, by analyzing previous information of users, advertisements, and queries; search engines estimate click-through rate (CTR) for predicting users’ clicks. CTR is a ratio of clicks to number of impressions associated with a particular advertisement. In this paper, we propose a model, based on CTR, to estimate probabilities of clicks using logistic regression that determines parameters using stochastic gradient ascent method (SGA). Moreover, this paper also summarizes the comparative analysis of SGA and batch gradient ascent (BGA) methods, in terms of accuracy and learning time.

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Author information

Authors and Affiliations

  1. Sarvajanik College of Engineering and Technology, Surat, India

    Jenish Dhanani & Keyur Rana

Authors
  1. Jenish Dhanani
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  2. Keyur Rana
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Corresponding author

Correspondence to Jenish Dhanani .

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Editors and Affiliations

  1. Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  2. Dagenham, United Kingdom

    Malaya Kumar Nayak

  3. Department of Information Technology, Sabar Institute of Technology, Ahmedabad, Gujarat, India

    Amit Joshi

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Dhanani, J., Rana, K. (2018). Logistic Regression with Stochastic Gradient Ascent to Estimate Click Through Rate. In: Mishra, D., Nayak, M., Joshi, A. (eds) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-3932-4_33

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  • DOI: https://doi.org/10.1007/978-981-10-3932-4_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3931-7

  • Online ISBN: 978-981-10-3932-4

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