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Product-Based Neural Networks for User Response Prediction

Qu, Y; Cai, H; Zhang, W; Wen, Y; Wang, J; (2017) Product-Based Neural Networks for User Response Prediction. In: Bonchi, F and Domingo-Ferrer, J and Baeza-Yates, R and Zhou, ZH and Wu, Z, (eds.) Proceedings of the 16th International Conference on Data Mining (ICDM). (pp. pp. 1149-1154). IEEE: Danvers (MA), USA. Green open access

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Abstract

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, webs earch 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. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfieldcategories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two-large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.

Type: Proceedings paper
Title: Product-Based Neural Networks for User Response Prediction
Event: Proceedings of the 16th International Conference on Data Mining (ICDM)
Location: Barcelona, Spain
Dates: 12th-15th December 2016
ISBN-13: 978-1-5090-5473-2
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICDM.2016.0151
Publisher version: https://doi.org/10.1109/ICDM.2016.0151
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Complexity theory, Predictive models, Advertising, Artificial neural networks, Encoding, Data models
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
URI: https://discovery.ucl.ac.uk/id/eprint/1524034
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