Convolutional Neural Networks based Click-Through Rate Prediction with Multiple Feature Sequences Article Swipe

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Patrick P. K. Chan , Xian Hu , Lili Zhao , Daniel Yeung , Dapeng Liu , Lei Xiao ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.24963/ijcai.2018/277

Convolutional Neural Network (CNN) achieved satisfying performance in click-through rate (CTR) prediction in recent studies. Since features used in CTR prediction have no meaningful sequence in nature, the features can be arranged in any order. As CNN learns the local information of a sample, the feature sequence may influence its performance significantly. However, this problem has not been fully investigated. This paper firstly investigates whether and how the feature sequence affects the performance of the CNN-based CTR prediction method. As the data distribution of CTR prediction changes with time, the best current sequence may not be suitable for future data. Two multi-sequence models are proposed to learn the information provided by different sequences. The first model learns all sequences using a single feature learning module, while each sequence is learnt individually by a feature learning module in the second one. Moreover, a method of generating a set of embedding sequences which aims to consider the combined influence of all feature pairs on feature learning is also introduced. The experiments are conducted to demonstrate the effectiveness and stability of our proposed models in the offline and online environment on both the benchmark Avazu dataset and a real commercial dataset.

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Computer Science

Convolutional Neural Network

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Artificial Intelligence

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Computer science Feature (linguistics) Convolutional neural network Benchmark (surveying) Sequence (biology) Artificial intelligence Sequence labeling Embedding Feature learning Pattern recognition (psychology) Stability (learning theory) Set (abstract data type) Artificial neural network Machine learning Geography Programming language Linguistics Genetics Economics Biology Philosophy Task (project management) Geodesy Management

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Type: article
Language: en
Landing Page: https://doi.org/10.24963/ijcai.2018/277
PDF: https://www.ijcai.org/proceedings/2018/0277.pdf
OA Status: gold
Cited By: 36
References: 24
Related Works: 10
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DOI: https://doi.org/10.24963/ijcai.2018/277

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Title: Convolutional Neural Networks based Click-Through Rate Prediction with Multiple Feature Sequences

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Language: en

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Publication year: 2018

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Publication date: 2018-07-01

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Authors: Patrick P. K. Chan, Xian Hu, Lili Zhao, Daniel Yeung, Dapeng Liu, Lei Xiao

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Landing page: https://doi.org/10.24963/ijcai.2018/277

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Concepts: Computer science, Feature (linguistics), Convolutional neural network, Benchmark (surveying), Sequence (biology), Artificial intelligence, Sequence labeling, Embedding, Feature learning, Pattern recognition (psychology), Stability (learning theory), Set (abstract data type), Artificial neural network, Machine learning, Geography, Programming language, Linguistics, Genetics, Economics, Biology, Philosophy, Task (project management), Geodesy, Management

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Cited by: 36

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