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Deep Interest Network for Click-Through Rate Prediction
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.
Deep Interest Evolution Network for Click-Through Rate Prediction
TLDR
This paper proposes a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction, which significantly outperforms the state-of-the-art solutions and design interest extractor layer to capture temporal interests from history behavior sequence.
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
TLDR
This paper model CVR in a brand-new perspective by making good use of sequential pattern of user actions, i.e., impression -> click -> conversion, which is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
TLDR
This paper faces directly the challenge of long sequential user behavior modeling and proposes a novel memory-based architecture named MIMN (Multi-channel user Interest Memory Network) to capture user interests from long sequential behavior data, achieving superior performance over state-of-the-art models.
Semantic Human Matting
TLDR
Semantic Human Matting (SHM) is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks and achieves comparable results with state-of-the-art interactive matting methods.
Optimized Cost per Click in Taobao Display Advertising
TLDR
A bid optimizing strategy called optimized cost per click (OCPC) is proposed which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity and yields substantially better results than previous fixed bid manner.
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
TLDR
This paper forms budget constrained bidding as a Markov Decision Process and proposes a model-free reinforcement learning framework to resolve the optimization problem, and employs a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively.
Learning Tree-based Deep Model for Recommender Systems
TLDR
A novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks is proposed and can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction.
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
TLDR
A Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user is proposed and adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs.
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
TLDR
A new cascaded search paradigm enables SIM with a better ability to model lifelong sequential behavior data in both scalability and accuracy, and also introduces the hands-on experience on how to implement SIM in large scale industrial systems.
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