Search Results for author: Junwei Pan

Found 28 papers, 10 papers with code

Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models

no code implementations5 Dec 2024 Yuhao Wang, Junwei Pan, Xiangyu Zhao, Pengyue Jia, Wanyu Wang, YuAn Wang, Yue Liu, Dapeng Liu, Jie Jiang

Sequential recommendation (SR) aims to model the sequential dependencies in users' historical interactions to better capture their evolving interests.

Sequential Recommendation

Towards Unifying Feature Interaction Models for Click-Through Rate Prediction

no code implementations19 Nov 2024 Yu Kang, Junwei Pan, Jipeng Jin, Shudong Huang, Xiaofeng Gao, Lei Xiao

Modeling feature interactions plays a crucial role in accurately predicting click-through rates (CTR) in advertising systems.

Click-Through Rate Prediction

Towards Scalable Semantic Representation for Recommendation

no code implementations12 Oct 2024 Taolin Zhang, Junwei Pan, Jinpeng Wang, Yaohua Zha, Tao Dai, Bin Chen, Ruisheng Luo, Xiaoxiang Deng, YuAn Wang, Ming Yue, Jie Jiang, Shu-Tao Xia

With recent advances in large language models (LLMs), there has been emerging numbers of research in developing Semantic IDs based on LLMs to enhance the performance of recommendation systems.

Recommendation Systems

Long-Sequence Recommendation Models Need Decoupled Embeddings

no code implementations3 Oct 2024 Ningya Feng, Junwei Pan, Jialong Wu, Baixu Chen, Ximei Wang, Qian Li, Xian Hu, Jie Jiang, Mingsheng Long

In this work, we identify and characterize, for the first time, a neglected deficiency in existing long-sequence recommendation models: a single set of embeddings struggles with learning both attention and representation, leading to interference between these two processes.

Recommendation Systems

Deep Pattern Network for Click-Through Rate Prediction

no code implementations17 Apr 2024 Hengyu Zhang, Junwei Pan, Dapeng Liu, Jie Jiang, Xiu Li

These patterns harbor substantial potential to significantly enhance CTR prediction performance.

Click-Through Rate Prediction Prediction +2

Understanding the Ranking Loss for Recommendation with Sparse User Feedback

1 code implementation21 Mar 2024 Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang

We introduce a novel perspective on the effectiveness of the auxiliary ranking loss in CTR prediction: it generates larger gradients on negative samples, thereby mitigating the optimization difficulties when using the BCE loss only and resulting in improved classification ability.

Binary Classification Click-Through Rate Prediction +1

Ads Recommendation in a Collapsed and Entangled World

1 code implementation22 Feb 2024 Junwei Pan, Wei Xue, Ximei Wang, Haibin Yu, Xun Liu, Shijie Quan, Xueming Qiu, Dapeng Liu, Lei Xiao, Jie Jiang

We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations.

Feature Correlation Model Optimization

On the Embedding Collapse when Scaling up Recommendation Models

2 code implementations6 Oct 2023 Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long

On the one hand, interacting with collapsed embeddings restricts embedding learning and exacerbates the collapse issue.

Diversity

Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning

no code implementations19 Sep 2023 Ximei Wang, Junwei Pan, Xingzhuo Guo, Dapeng Liu, Jie Jiang

Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains.

Recommendation Systems

STEM: Unleashing the Power of Embeddings for Multi-task Recommendation

2 code implementations16 Aug 2023 Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang

Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks.

Multi-Task Learning Recommendation Systems

Temporal Interest Network for User Response Prediction

2 code implementations15 Aug 2023 Haolin Zhou, Junwei Pan, Xinyi Zhou, Xihua Chen, Jie Jiang, Xiaofeng Gao, Guihai Chen

To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target.

Click-Through Rate Prediction Prediction +1

ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning

1 code implementation NeurIPS 2023 Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Liu Dapeng, Jie Jiang, Mingsheng Long

Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks.

AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning

no code implementations28 Nov 2022 Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, Guibing Guo

In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.

Multi-Task Learning Recommendation Systems

AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling

no code implementations27 Oct 2022 Zuowu Zheng, Xiaofeng Gao, Junwei Pan, Qi Luo, Guihai Chen, Dapeng Liu, Jie Jiang

In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields.

Click-Through Rate Prediction

Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach

no code implementations25 Apr 2022 Chenxiao Yang, Qitian Wu, Jipeng Jin, Xiaofeng Gao, Junwei Pan, Guihai Chen

To circumvent false negatives, we develop a principled approach to improve the reliability of negative instances and prove that the objective is an unbiased estimation of sampling from the true negative distribution.

Collaborative Filtering

Impression Allocation and Policy Search in Display Advertising

no code implementations11 Mar 2022 Di wu, Cheng Chen, Xiujun Chen, Junwei Pan, Xun Yang, Qing Tan, Jian Xu, Kuang-Chih Lee

In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable.

Multi-agent Reinforcement Learning

Cross-Task Knowledge Distillation in Multi-Task Recommendation

no code implementations20 Feb 2022 Chenxiao Yang, Junwei Pan, Xiaofeng Gao, Tingyu Jiang, Dapeng Liu, Guihai Chen

Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e. g, click, purchase) are treated as individual tasks and jointly trained with a unified model.

Knowledge Distillation Multi-Task Learning +1

Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback

1 code implementation13 Aug 2021 Haoming Li, Feiyang Pan, Xiang Ao, Zhao Yang, Min Lu, Junwei Pan, Dapeng Liu, Lei Xiao, Qing He

The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.

An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

no code implementations12 Jul 2021 Tian Zhou, Hao He, Shengjun Pan, Niklas Karlsson, Bharatbhushan Shetty, Brendan Kitts, Djordje Gligorijevic, San Gultekin, Tingyu Mao, Junwei Pan, Jianlong Zhang, Aaron Flores

Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.

$FM^2$: Field-matrixed Factorization Machines for Recommender Systems

2 code implementations20 Feb 2021 Yang Sun, Junwei Pan, Alex Zhang, Aaron Flores

The FmFM model's performance is also comparable to DNN models which require much more FLOPs in runtime.

Click-Through Rate Prediction Recommendation Systems

Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising

no code implementations5 Dec 2020 Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai

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.

Bid Shading by Win-Rate Estimation and Surplus Maximization

no code implementations19 Sep 2020 Shengjun Pan, Brendan Kitts, Tian Zhou, Hao He, Bharatbhushan Shetty, Aaron Flores, Djordje Gligorijevic, Junwei Pan, Tingyu Mao, San Gultekin, Jianlong Zhang

We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.

Attribute

Bid Shading in The Brave New World of First-Price Auctions

no code implementations2 Sep 2020 Djordje Gligorijevic, Tian Zhou, Bharatbhushan Shetty, Brendan Kitts, Shengjun Pan, Junwei Pan, Aaron Flores

Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth.

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving

2 code implementations17 Feb 2020 Wei Deng, Junwei Pan, Tian Zhou, Deguang Kong, Aaron Flores, Guang Lin

To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.

Click-Through Rate Prediction Prediction

A Batched Multi-Armed Bandit Approach to News Headline Testing

no code implementations17 Aug 2019 Yizhi Mao, Miao Chen, Abhinav Wagle, Junwei Pan, Michael Natkovich, Don Matheson

At Yahoo Front Page, headline testing is carried out using a test-rollout strategy: we first allocate equal proportion of the traffic to each headline variation for a defined testing period, and then shift all future traffic to the best-performing variation.

Thompson Sampling

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

no code implementations24 Jul 2019 Junwei Pan, Yizhi Mao, Alfonso Lobos Ruiz, Yu Sun, Aaron Flores

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.

Multi-Task Learning Prediction

Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

5 code implementations9 Jun 2018 Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu

The data involved in CTR prediction are typically multi-field categorical data, i. e., every feature is categorical and belongs to one and only one field.

Click-Through Rate Prediction Prediction

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