Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising
نویسندگان
چکیده
منابع مشابه
Deep Interest Network for Click-Through Rate Prediction
To better extract users’ interest by exploiting the rich historical behavior data is crucial for building the click-through rate (CTR) prediction model in the online advertising system in e-commerce industry. There are two key observations on user behavior data: i) diversity. Users are interested in different kinds of goods when visiting e-commerce site. ii) local activation. Whether users clic...
متن کاملPBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
We describe a parallel bayesian online deep learning framework (PBODL) for clickthrough rate (CTR) prediction within today’s Tencent advertising system, which provides quick and accurate learning of user preferences. We first explain the framework with a deep probit regression model, which is trained with probabilistic back-propagation in the mode of assumed Gaussian density filtering. Then we ...
متن کاملClick-Through Rate Estimation for Rare Events in Online Advertising
In online advertising campaigns, to measure purchase propensity, click-through rate (CTR), defined as a ratio of number of clicks to number of impressions, is one of the most informative metrics used in business activities such as performance evaluation and budget planning. No matter what channel an ad goes through (display ads, sponsored search or contextual advertising), CTR estimation for ra...
متن کاملOnline Limited-Memory BFGS for Click-Through Rate Prediction
We study the problem of click-through rate (CTR) prediction, where the goal is to predict the probability that a user will click on a search advertisement given information about his issued query and account. In this paper, we formulate a model for CTR prediction using logistic regression, then assess the performance of stochastic gradient descent (SGD) and online limited-memory BFGS (oLBFGS) f...
متن کاملA New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets
In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: 2169-3536
DOI: 10.1109/access.2021.3049827