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Found 55 papers, 22 papers with code
KuaiSim: A Comprehensive Simulator for Recommender Systems
For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.
Unified Language-Vision Pretraining with Dynamic Discrete Visual Tokenization
To this end, we craft a visual tokenizer that translates the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.
A Large Language Model Enhanced Conversational Recommender System
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback.
Graph Contrastive Learning with Generative Adversarial Network
Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users.
State Regularized Policy Optimization on Data with Dynamics Shift
Data with dynamics shift are separated according to their environment parameters to train the corresponding policy.
Generative Flow Network for Listwise Recommendation
In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.
Multi-behavior Self-supervised Learning for Recommendation
Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task.
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors.
Disentangled Causal Embedding With Contrastive Learning For Recommender System
Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results.
Multi-Task Recommendations with Reinforcement Learning
To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.
Exploration and Regularization of the Latent Action Space in Recommendation
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness.
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost.
Two-Stage Constrained Actor-Critic for Short Video Recommendation
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
Reinforcing User Retention in a Billion Scale Short Video Recommender System
In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance.
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1\% in multiple task metrics across multiple domains.
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement
Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.
Real-time Short Video Recommendation on Mobile Devices
However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate.
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation.
Truncation-Free Matching System for Display Advertising at Alibaba
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
COLD: Towards the Next Generation of Pre-Ranking System
We name it COLD (Computing power cost-aware Online and Lightweight Deep pre-ranking system).
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
Learning Optimal Tree Models Under Beam Search
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
DCAF: A Dynamic Computation Allocation Framework for Online Serving System
These stages usually allocate resource manually with specific computing power budgets, which requires the serving configuration to adapt accordingly.
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
Serving the main traffic in our real system now, SIM models user behavior data with maximum length reaching up to 54000, pushing SOTA to 54x.
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
A Deep Recurrent Survival Model for Unbiased Ranking
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.
Progressive Feature Polishing Network for Salient Object Detection
Feature matters for salient object detection.
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
We refer to this process as Leverage.
Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling
In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands.
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder
In fact, this objective term guides the encoder towards the "best encoder" of the decoder to enhance the expressiveness.
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index.
Deep Interest Evolution Network for Click-Through Rate Prediction
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt
Ranked #1 on
Click-Through Rate Prediction
on Amazon Dataset
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising
With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Adaptive Display Exposure for Real-Time Advertising
In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?
Semantic Human Matting
SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks.
Ranked #4 on
Image Matting
on AIM-500
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Real-time advertising allows advertisers to bid for each impression for a visiting user.
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint.
Learning Tree-based Deep Model for Recommender Systems
In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult.
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.
Deep Interest Network for Click-Through Rate Prediction
In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.
Ranked #1 on
Click-Through Rate Prediction
on Amazon
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
Optimized Cost per Click in Taobao Display Advertising
Moreover, the platform has to be responsible for the business revenue and user experience.
Learning Kernels with Radiuses of Minimum Enclosing Balls
Experiments show that our method significantly outperforms both SVM with the uniform combination of basis kernels and other state-of-art MKL approaches.
