DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks

By Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, Zhongfei Zhang. 2016

120 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fangzhao Wu
2. Chuhan Wu
3. Xing Xie
4. Yongfeng Huang
5. Matthew Lease
6. Ye Zhang
7. Byron C. Wallace
8. Kun Gai

In Information Systems

1. Fangzhao Wu
2. Chuhan Wu
3. Yongfeng Huang
4. Xing Xie
5. Matthew Lease
6. Kun Zhang
7. Byron C. Wallace
8. Ye Zhang

Citations in the corpus

• Deep Interest Network for Click-Through Rate Prediction (2017, methodology): “ystem. Driven by the success of deep learning in image recognition, computer vision and natural language processing, a number of deep learning based methods have been proposed for CTR prediction task [1, 2, 3, 4]. These methods usually rst employ embedding layer on the input, mapping original large ”
• NPA: Neural News Recommendation with Personalized Attention (2019, methodology): “huge, the computational cost of these methods is usually heavy during model training. Thus, these methods are sub-optimal for simulating realworld news recommendation scenarios. Motivated by [13] and [38], we propose to apply negative sampling techniques by jointly predicting the click score for K+1”
• Neural information retrieval: at the end of the early years (2017, background): “Explicit The goal of Zhang et al. (2014) is to efficiently retrieve passages that are semantically similar to a query, making use of hashing methods on word vectors that are learned in advance.”
• ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation (2017, background): “…many other tasks like reading comprehension (Cui et al. 2016; Cheng, Dong, and Lapata 2016; Lin et al. 2017), ads recommendation (Zhou et al. 2017; Zhai et al. 2016), computer vision (Xu et al. 2015), etc. Self-attentions are also studied in different mechanisms (Vaswani et al. 2017; Lin et al.…”
• Deep learning detecting fraud in credit card transactions (2018, methodology): “More recently, Deep Learning algorithms have been used in areas that require the prediction of human behavior such as customer relationship management, recommendation systems and mobile advertising [5]-[7].”
• Neural News Recommendation with Long- and Short-term User Representations (2019, methodology): “Motivated by (Huang et al., 2013) and (Zhai et al., 2016), we propose to use the negative sampling technique for model training.”
• Neural News Recommendation with Attentive Multi-View Learning (2019, methodology): “Motivated by [ Huang et al. , 2013 ] and [ Zhai et al. , 2016 ] , we propose to use negative sampling techniques for model training.”
• Disease Prediction by Machine Learning Over Big Data From Healthcare Communities (2017, background): “The ratio of the training set and the test set is 6:1 [22], [23], i.”
• Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction (2020, background): “a up to 54000, 54x larger than MIMN, the published SOTA industry solution for this task. 2 RELATED WORK UserInterestModel.Deep learning based methods have achieved great success in CTR prediction task[1, 11, 18]. In early age, most pioneer works[1, 4, 7, 9, 15] use a deep neural network to capture i”