Deep &amp; Cross Network for Ad Click Predictions

Wang, Ruoxi; Fu, Bin; Fu, Gang; Wang, Mingliang

doi:10.1145/3124749.3124754

Cited by 905 publications

(712 citation statements)

References 15 publications

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“…Relative Improvement (RI). It should be noted that a small improvement with respect to AUC is regarded significant for realworld CTR tasks [2,6,15,30]. In order to estimate the relative improvement of our model achieves over the compared models, we here measure RI-AUC and RI-Logloss, which can be formulated as,…”

Section: Discussionmentioning

confidence: 99%

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Fi-GNN

Cui

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

139

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Click-through rate (CTR) prediction is an essential task in web applications such as online advertising and recommender systems, whose features are usually in multi-field form. The key of this task is to model feature interactions among different feature fields. Recently proposed deep learning based models follow a general paradigm: raw sparse input multi-filed features are first mapped into dense field embedding vectors, and then simply concatenated together to feed into deep neural networks (DNN) or other specifically designed networks to learn high-order feature interactions. However, the simple unstructured combination of feature fields will inevitably limit the capability to model sophisticated interactions among different fields in a sufficiently flexible and explicit fashion.In this work, we propose to represent the multi-field features in a graph structure intuitively, where each node corresponds to a feature field and different fields can interact through edges. The task of modeling feature interactions can be thus converted to modeling node interactions on the corresponding graph. To this end, we design a novel model Feature Interaction Graph Neural Networks (Fi-GNN). Taking advantage of the strong representative power of graphs, our proposed model can not only model sophisticated feature interactions in a flexible and explicit fashion, but also provide good model explanations for CTR prediction. Experimental results on two real-world datasets show its superiority over the state-of-the-arts.

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Section: Discussionmentioning

confidence: 99%

“…CrossNet (Deep&Cross) [30] (C) is the core of Deep&Cross model, which tries to model feature interactions explicitly by taking outer product of concatenated feature vector at the bit-wise level.…”

Section: Discussionmentioning

confidence: 99%

Fi-GNN

Cui

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

139

View full text Add to dashboard Cite

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“…Research on click prediction focus on developing various models, including Logistic Regression (LR) [8,23,27], Polynomial-2 (Poly2) [6], tree-based models [16], tensor-based models [26], Bayesian models [13], Field-aware Factorization Machines (FFM) [18,19], and Field-weighted Factorization Machines (FwFM) [24]. Recently, deep learning for CTR prediction also attracted a lot of research attention [9,14,15,25,29,30,32].…”

Section: Related Workmentioning

confidence: 99%

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Pan

Mao

Ruiz

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry. Unlike click prediction, conversions have different types in nature, and each type may be associated with different decisive factors. In this paper, we formulate conversion prediction as a multi-task learning problem, so that the prediction models for different types of conversions can be learned together. These models share feature representations, but have their specific parameters, providing the benefit of information-sharing across all tasks. We then propose Multi-Task Field-weighted Factorization Machine (MT-FwFM) to solve these tasks jointly. Our experiment results show that, compared with two state-of-the-art models, MT-FwFM improve the AUC by 0.74% and 0.84% on two conversion types, and the weighted AUC across all conversion types is also improved by 0.50%.

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“…CTR prediction has attracted lots of attention from both academia and industry [2,8,9,17,20]. For example, Logistic Regression (LR) [15] models linear feature importance.…”

Section: Introductionmentioning

confidence: 99%

Click-through rate prediction with the user memory network

Ouyang

Zhang

Ren

et al. 2019

Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data

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Click-through rate (CTR) prediction is a critical task in online advertising systems. Models like Deep Neural Networks (DNNs) are simple but stateless. They consider each target ad independently and cannot directly extract useful information contained in users' historical ad impressions and clicks. In contrast, models like Recurrent Neural Networks (RNNs) are stateful but complex. They model temporal dependency between users' sequential behaviors and can achieve improved prediction performance than DNNs. However, both the offline training and online prediction process of RNNs are much more complex and time-consuming. In this paper, we propose Memory Augmented DNN (MA-DNN) for practical CTR prediction services. In particular, we create two external memory vectors for each user, memorizing high-level abstractions of what a user possibly likes and dislikes. The proposed MA-DNN achieves a good compromise between DNN and RNN. It is as simple as DNN, but has certain ability to exploit useful information contained in users' historical behaviors as RNN. Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services. Actually, the memory component can be augmented to other models as well (e.g., the Wide&Deep model).

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Deep & Cross Network for Ad Click Predictions

Cited by 905 publications

References 15 publications

Fi-GNN

Fi-GNN

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

Click-through rate prediction with the user memory network

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