Deep Interest Network for Click-Through Rate Prediction
Guorui Zhou,Xiaoqiang Zhu,Chenru Song,Ying Fan,Han Zhu,Xiao Ma,Yan Yanghui,Junqi Jin,Han Li,Kun Gai +9 more
- pp 1059-1068
Reads0
Chats0
TLDR
A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.Abstract:
Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.read more
Citations
More filters
Journal ArticleDOI
Top-aware reinforcement learning based recommendation
TL;DR: A Supervised deep Reinforcement learning Recommendation framework named as SRR is proposed, which utilizes a supervised learning model to partially guide the learning of recommendation policy, where the supervision signal and RL signal are jointly employed and updated in a complementary fashion.
Proceedings ArticleDOI
Knowledge-aware Complementary Product Representation Learning
TL;DR: Zhang et al. as mentioned in this paper used knowledge-aware learning with dual product embedding to detect complementary relationships directly from noisy and sparse customer purchase activities, and adopted the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by separating hyperplane theory.
Proceedings ArticleDOI
Generative Adversarial Framework for Cold-Start Item Recommendation
TL;DR: This work proposes a general framework named Generative Adversarial Recommendation (GAR), which can have similar distribution as the warm embeddings that can even fool the recommender, and has strong overall recommendation performance in cold-starting both the CF-based model (improved by over 30.18%) and the GNN- based model ( improved by over 17.78%).
Journal ArticleDOI
Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online Advertising
TL;DR: Zhang et al. as mentioned in this paper proposed Deep User Segment Interest Network (DUSIN) model to improve CTR prediction by predicting the change of interest of a user based on other users' change of interests.
Journal ArticleDOI
JointCTR: a joint CTR prediction framework combining feature interaction and sequential behavior learning
TL;DR: A modular click-through rate prediction framework JointCTR is proposed, which yields competitive performance compared to state-of-the-art models and almost all different types of popular models can be assembled into it, that shows the flexibility and scalability of the framework.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.