Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.
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CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.
Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
#2 best model for Click-Through Rate Prediction on Bing News
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.
#2 best model for Click-Through Rate Prediction on Company*
In this work, we propose mixed dimension embedding layers in which the dimension of a particular embedding vector can depend on the frequency of the item.
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.
To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.
The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent.
Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.