Papers › Deep & Cross Network for Ad Click Predictions

Deep & Cross Network for Ad Click Predictions

17 Aug 2017arXiv:1708.05123archive 2025-07-28

Ruoxi Wang, Bin Fu, Gang Fu, Mingliang Wang

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. DNNs are able to automatically learn feature interactions; however, they generate all the interactions implicitly, and are not necessarily efficient in learning all types of cross features. In this paper, we propose the Deep & Cross Network (DCN) which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions. In particular, DCN explicitly applies feature crossing at each layer, requires no manual feature engineering, and adds negligible extra complexity to the DNN model. Our experimental results have demonstrated its superiority over the state-of-art algorithms on the CTR prediction dataset and dense classification dataset, in terms of both model accuracy and memory usage.

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JianzhouZhan/Awesome-RecSystem-Models mentioned on GitHubpytorchMIT report
Snail110/recsys mentioned on GitHubtf report
UlionTse/mlgb mentioned on GitHubpytorch report
bytedance/largebatchctr mentioned on GitHubtfApache-2.0 report
shenweichen/DeepCTR mentioned on GitHubtf report
shenweichen/DeepCTR-Torch mentioned on GitHubpytorchApache-2.0 report
tangxyw/RecAlgorithm mentioned on GitHubtfBSD-2-Clause report
wangweitong/DL mentioned on GitHubpytorch report
wangweitong/recommend_system mentioned on GitHubpytorchMIT report
xue-pai/FuxiCTR mentioned on GitHubpytorch report

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1ran · honoured contract
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Tasks

Click-Through Rate PredictionFeature Engineering

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