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# Click-Through Rate Prediction Edit

16 papers with code · Miscellaneous

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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# AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

29 Oct 2018shenweichen/DeepCTR

The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features (a.k.a. Our proposed algorithm is very general, which can be applied to both numerical and categorical input features.

# Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

1 Jul 2018shenweichen/DeepCTR

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding.

# DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

12 Apr 2018shenweichen/DeepCTR

In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data. We conduct online A/B test in Huawei App Market, which reveals that DeepFM-D leads to more than 10% improvement of click-through rate in the production environment, compared to a well-engineered LR model.

# xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

14 Mar 2018shenweichen/DeepCTR

With the great success of deep neural networks (DNNs) in various fields, recently researchers have proposed several DNN-based factorization model to learn both low- and high-order feature interactions. On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.

# Deep & Cross Network for Ad Click Predictions

17 Aug 2017shenweichen/DeepCTR

Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching.

# Deep Interest Network for Click-Through Rate Prediction

21 Jun 2017shenweichen/DeepCTR

Click-through rate prediction is an essential task in industrial applications, such as online advertising. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.

# Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction

18 Apr 2017shenweichen/DeepCTR

CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. We formulate the learning problem with $L_1$ and $L_{2,1}$ regularizers, leading to a non-convex and non-smooth optimization problem.

# DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

13 Mar 2017shenweichen/DeepCTR

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering.

# Product-based Neural Networks for User Response Prediction

1 Nov 2016shenweichen/DeepCTR

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. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding.

# Wide & Deep Learning for Recommender Systems

24 Jun 2016shenweichen/DeepCTR

Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features.