Search Results for author: Defu Lian

Found 60 papers, 29 papers with code

Matching-oriented Embedding Quantization For Ad-hoc Retrieval

1 code implementation EMNLP 2021 Shitao Xiao, Zheng Liu, Yingxia Shao, Defu Lian, Xing Xie

In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.

Quantization Retrieval

RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability

no code implementations18 Nov 2023 Yuxuan Lei, Jianxun Lian, Jing Yao, Xu Huang, Defu Lian, Xing Xie

Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training.

Explanation Generation Instruction Following +1

Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction

no code implementations15 Nov 2023 Qi Liu, Xuyang Hou, Haoran Jin, Jin Chen, Zhe Wang, Defu Lian, Tan Qu, Jia Cheng, Jun Lei

The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy.

Click-Through Rate Prediction

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting

1 code implementation10 Nov 2023 Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Defu Lian, Ning An, Longbing Cao, Zhendong Niu

FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.

Time Series Time Series Forecasting

APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

1 code implementation6 Nov 2023 Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong liu, Ruiming Tang, Defu Lian, Enhong Chen

To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.

Graph Learning Multi-Task Learning +1

1DFormer: Learning 1D Landmark Representations via Transformer for Facial Landmark Tracking

no code implementations1 Nov 2023 Shi Yin, Shijie Huan, Defu Lian, Shangfei Wang, Jinshui Hu, Tao Guo, Bing Yin, BaoCai Yin, Cong Liu

For temporal modeling, we propose a recurrent token mixing mechanism, an axis-landmark-positional embedding mechanism, as well as a confidence-enhanced multi-head attention mechanism to adaptively and robustly embed long-term landmark dynamics into their 1D representations; for structure modeling, we design intra-group and inter-group structure modeling mechanisms to encode the component-level as well as global-level facial structure patterns as a refinement for the 1D representations of landmarks through token communications in the spatial dimension via 1D convolutional layers.

Landmark Tracking

A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems

no code implementations20 Oct 2023 Xu Huang, Jianxun Lian, Hao Wang, Defu Lian, Xing Xie

Recommendation systems effectively guide users in locating their desired information within extensive content repositories.

Fairness Recommendation Systems

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt

1 code implementation19 Oct 2023 Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Y. Zhang, Jun Zhou, Defu Lian, Ying WEI

In this work, we first investigate such anytime fine-tuning effectiveness of existing continual pre-training approaches, concluding with unanimously decreased performance on unseen domains.

Transfer Learning

Large-Scale OD Matrix Estimation with A Deep Learning Method

no code implementations9 Oct 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints.

Toward Robust Recommendation via Real-time Vicinal Defense

no code implementations29 Sep 2023 Yichang Xu, Chenwang Wu, Defu Lian

Recommender systems have been shown to be vulnerable to poisoning attacks, where malicious data is injected into the dataset to cause the recommender system to provide biased recommendations.

Recommendation Systems

Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias

1 code implementation26 Sep 2023 Zhihao Shi, Jie Wang, Fanghua Lu, Hanzhu Chen, Defu Lian, Zheng Wang, Jieping Ye, Feng Wu

The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias.

Representation Learning

Interactive Graph Convolutional Filtering

no code implementations4 Sep 2023 Jin Zhang, Defu Lian, Hong Xie, Yawen Li, Enhong Chen

Furthermore, we employ Bayesian meta-learning methods to effectively address the cold-start problem and derive theoretical regret bounds for our proposed method, ensuring a robust performance guarantee.

Collaborative Filtering Meta-Learning +2

Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations

no code implementations31 Aug 2023 Xu Huang, Jianxun Lian, Yuxuan Lei, Jing Yao, Defu Lian, Xing Xie

We introduce an efficient framework called InteRecAgent, which employs LLMs as the brain and recommender models as tools.

Recommendation Systems

KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification

no code implementations15 Aug 2023 Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, Defu Lian, Mengdi Zhang, Enhong Chen

However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i. e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels.

Node Classification Representation Learning +1

Deep Task-specific Bottom Representation Network for Multi-Task Recommendation

no code implementations11 Aug 2023 Qi Liu, Zhilong Zhou, Gangwei Jiang, Tiezheng Ge, Defu Lian

In this paper, we focus on the bottom representation learning of MTL in RS and propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem.

Multi-Task Learning Recommendation Systems +1

A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence

no code implementations11 Jul 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization.

Learning to Substitute Spans towards Improving Compositional Generalization

1 code implementation5 Jun 2023 Zhaoyi Li, Ying WEI, Defu Lian

Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization.

Data Augmentation Inductive Bias +1

GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation

no code implementations1 Mar 2023 Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, Enhong Chen

Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item.

Contrastive Learning Sequential Recommendation

Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation

no code implementations15 Nov 2022 Zhihao Zhu, Chenwang Wu, Min Zhou, Hao Liao, Defu Lian, Enhong Chen

Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications.

Adversarial Attack

Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting

2 code implementations30 Oct 2022 Leyan Deng, Chenwang Wu, Defu Lian, Min Zhou

In this technical report, we present our solutions to the Traffic4cast 2022 core challenge and extended challenge.

feature selection Graph Attention

Towards Robust Recommender Systems via Triple Cooperative Defense

no code implementations25 Oct 2022 Qingyang Wang, Defu Lian, Chenwang Wu, Enhong Chen

Notably, TCD adds pseudo label data instead of deleting abnormal data, which avoids the cleaning of normal data, and the cooperative training of the three models is also beneficial to model generalization.

Pseudo Label Recommendation Systems

Boosting Factorization Machines via Saliency-Guided Mixup

1 code implementation17 Jun 2022 Chenwang Wu, Defu Lian, Yong Ge, Min Zhou, Enhong Chen, DaCheng Tao

Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM).

Recommendation Systems

Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever

no code implementations30 May 2022 Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang, Kai Zheng, Enhong Chen

Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss.

Self-Supervised Text Erasing with Controllable Image Synthesis

no code implementations27 Apr 2022 Gangwei Jiang, Shiyao Wang, Tiezheng Ge, Yuning Jiang, Ying WEI, Defu Lian

The synthetic training images with erasure ground-truth are then fed to train a coarse-to-fine erasing network.

Image Generation

BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction

1 code implementation18 Apr 2022 Bisheng Li, Min Zhou, Shengzhong Zhang, Menglin Yang, Defu Lian, Zengfeng Huang

Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information.

Graph Classification Link Prediction +1

HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization

1 code implementation18 Apr 2022 Menglin Yang, Min Zhou, Jiahong Liu, Defu Lian, Irwin King

Hyperbolic space offers a spacious room to learn embeddings with its negative curvature and metric properties, which can well fit data with tree-like structures.

Collaborative Filtering Recommendation Systems

A Mutually Reinforced Framework for Pretrained Sentence Embeddings

no code implementations28 Feb 2022 Junhan Yang, Zheng Liu, Shitao Xiao, Jianxun Lian, Lijun Wu, Defu Lian, Guangzhong Sun, Xing Xie

Instead of relying on annotation heuristics defined by humans, it leverages the sentence representation model itself and realizes the following iterative self-supervision process: on one hand, the improvement of sentence representation may contribute to the quality of data annotation; on the other hand, more effective data annotation helps to generate high-quality positive samples, which will further improve the current sentence representation model.

Contrastive Learning Sentence Embeddings

Reinforcement Routing on Proximity Graph for Efficient Recommendation

no code implementations23 Jan 2022 Chao Feng, Defu Lian, Xiting Wang, Zheng Liu, Xing Xie, Enhong Chen

Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.

Imitation Learning Recommendation Systems

Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

2 code implementations14 Jan 2022 Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Yingxia Shao, Defu Lian, Chaozhuo Li, Hao Sun, Denvy Deng, Liangjie Zhang, Qi Zhang, Xing Xie

In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification.

Quantization Retrieval

Meta-learning with an Adaptive Task Scheduler

2 code implementations NeurIPS 2021 Huaxiu Yao, Yu Wang, Ying WEI, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn

In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.

Drug Discovery Meta-Learning

Learned Index with Dynamic $\epsilon$

no code implementations29 Sep 2021 Daoyuan Chen, Wuchao Li, Yaliang Li, Bolin Ding, Kai Zeng, Defu Lian, Jingren Zhou

We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method.


Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering

1 code implementation13 Sep 2021 Jin Chen, Defu Lian, Binbin Jin, Xu Huang, Kai Zheng, Enhong Chen

Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering.

Collaborative Filtering

Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention

no code implementations28 May 2021 Yongji Wu, Lu Yin, Defu Lian, Mingyang Yin, Neil Zhenqiang Gong, Jingren Zhou, Hongxia Yang

With the rapid development of these services in the last two decades, users have accumulated a massive amount of behavior data.

Sequential Recommendation

Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation

1 code implementation28 May 2021 Yongji Wu, Defu Lian, Neil Zhenqiang Gong, Lu Yin, Mingyang Yin, Jingren Zhou, Hongxia Yang

Inspired by the idea of vector quantization that uses cluster centroids to approximate items, we propose LISA (LInear-time Self Attention), which enjoys both the effectiveness of vanilla self-attention and the efficiency of sparse attention.

Quantization Sequential Recommendation

Assessing Dialogue Systems with Distribution Distances

1 code implementation Findings (ACL) 2021 Jiannan Xiang, Yahui Liu, Deng Cai, Huayang Li, Defu Lian, Lemao Liu

An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems.

Dialogue Evaluation

GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph

1 code implementation NeurIPS 2021 Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, Xing Xie

The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information.

Language Modelling Recommendation Systems +1

A Unified Transferable Model for ML-Enhanced DBMS

1 code implementation6 May 2021 Ziniu Wu, Pei Yu, Peilun Yang, Rong Zhu, Yuxing Han, Yaliang Li, Defu Lian, Kai Zeng, Jingren Zhou

We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.


Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks

no code implementations22 Apr 2021 Junhan Yang, Zheng Liu, Bowen Jin, Jianxun Lian, Defu Lian, Akshay Soni, Eun Yong Kang, Yajun Wang, Guangzhong Sun, Xing Xie

For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.


Matching-oriented Product Quantization For Ad-hoc Retrieval

2 code implementations16 Apr 2021 Shitao Xiao, Zheng Liu, Yingxia Shao, Defu Lian, Xing Xie

In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.

Quantization Retrieval

Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure

1 code implementation2 Mar 2021 Jin Chen, Tiezheng Ge, Gangwei Jiang, Zhiqiang Zhang, Defu Lian, Kai Zheng

Based on the tree structure, Thompson sampling is adapted with dynamic programming, leading to efficient exploration for potential ad creatives with the largest CTR.

Efficient Exploration Thompson Sampling

Automated Creative Optimization for E-Commerce Advertising

1 code implementation28 Feb 2021 Jin Chen, Ju Xu, Gangwei Jiang, Tiezheng Ge, Zhiqiang Zhang, Defu Lian, Kai Zheng

However, interactions between creative elements may be more complex than the inner product, and the FM-estimated CTR may be of high variance due to limited feedback.

AutoML Click-Through Rate Prediction +2

Deep Pairwise Hashing for Cold-start Recommendation

no code implementations2 Nov 2020 Yan Zhang, Ivor W. Tsang, Hongzhi Yin, Guowu Yang, Defu Lian, Jingjing Li

Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation.


Sampling-Decomposable Generative Adversarial Recommender

no code implementations NeurIPS 2020 Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen

The GAN-style recommenders (i. e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective.

Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation

no code implementations24 May 2020 Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang

The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase.

Test Transfer Learning

GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions

1 code implementation12 May 2020 Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xuemin Lin

We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.

Lightrec: A memory and search-efficient recommender system

1 code implementation International World Wide Web Conference 2020 Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen, Xing Xie

On top of such a structure, LightRec will have an item represented as additive composition of B codewords, which are optimally selected from each of the codebooks.

Recommendation Systems

Binarized Graph Neural Network

no code implementations19 Apr 2020 Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xiangjian He, Yiguang Lin, Xuemin Lin

Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.

Graph Embedding

Binarized Collaborative Filtering with Distilling Graph Convolutional Networks

no code implementations5 Jun 2019 Haoyu Wang, Defu Lian, Yong Ge

Then we distill the ranking information derived from GCN into binarized collaborative filtering, which makes use of binary representation to improve the efficiency of online recommendation.

Collaborative Filtering Recommendation Systems

MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network

no code implementations27 May 2019 Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, Wen Su

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention.

Multi-Task Learning Representation Learning

A Survey on Session-based Recommender Systems

1 code implementation13 Feb 2019 Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Defu Lian

In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.

Collaborative Filtering Decision Making +1

Binarized Attributed Network Embedding

2 code implementations ICDM 2018 Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang

To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.

Graph Embedding Link Prediction +2

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