ECIR16 on our early results of Deep learning to estimating user responses

Home > Computational Advertising, ECIR2016 > ECIR16 on our early results of Deep learning to estimating user responses (clicks/conversions) ECIR16 on our early results of Deep learning to estimating user responses (clicks/conversions) February 25th, 2016 Predicting user responses, such as click-through rate and conversion rate, are critical in many web applications including web search, personalised recommendation, and online advertising. Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known. Major user response prediction models have to either limit themselves to linear models or require manually building up high-order combination features. The former loses the ability of exploring feature interactions, while the latter results…


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