Search Results for author:
Found 136 papers, 72 papers with code
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.
Matching-oriented Embedding Quantization For Ad-hoc Retrieval
In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.
Continual Learning on Dynamic Graphs via Parameter Isolation
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Embedding models have shown great power in knowledge graph completion (KGC) task.
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
In this paper, we introduce the imprecise label learning (ILL) framework, a unified approach to handle various imprecise label configurations, which are commonplace challenges in machine learning tasks.
Ranked #1 on
Learning with noisy labels
on mini WebVision 1.0
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark
Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can benefit various natural language processing (NLP) tasks for customers.
Towards Explainable Collaborative Filtering with Taste Clusters Learning
In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.
Exploring Vision-Language Models for Imbalanced Learning
However, their performance on imbalanced dataset is relatively poor, where the distribution of classes in the training dataset is skewed, leading to poor performance in predicting minority classes.
IRGen: Generative Modeling for Image Retrieval
While generative modeling has been ubiquitous in natural language processing and computer vision, its application to image retrieval remains unexplored.
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications.
Distillation from Heterogeneous Models for Top-K Recommendation
Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy.
FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning
Concretely, we design an attention-based adapter for the large model, CLIP, and the rest operations merely depend on adapters.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality.
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation
Augmented by only self-generated pseudo text, generation models over-emphasize exploitation of the previously learned space, suffering from a constrained generalization boundary.
CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection
In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost.
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
In this paper, we move towards combining large parametric models with non-parametric prototypical networks.
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.
Natural Language Understanding
Out-of-Distribution Generalization
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention
In this work, we dig into the intrinsic mechanism of this problem and found that sparser attention values in Transformer could improve diversity.
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Since at each round, the number of tunable parameters optimized on the server side equals the number of participating clients (thus independent of the model size), we are able to train a global model with massive parameters using only a small amount of proxy data (e. g., around one hundred samples).
FIXED: Frustratingly Easy Domain Generalization with Mixup
Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.
Learning on Large-scale Text-attributed Graphs via Variational Inference
In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.
Ranked #1 on
Node Property Prediction
on ogbn-papers100M
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation
We demonstrate that TRACE could enhance the entanglement of each segment and preceding latent variables and deduce a non-zero lower bound of the KL term, providing a theoretical guarantee of generation diversity.
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.
Test-Time Training for Graph Neural Networks
To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.
Effective and Efficient Query-aware Snippet Extraction for Web Search
In this paper, we propose an effective query-aware webpage snippet extraction method named DeepQSE, aiming to select a few sentences which can best summarize the webpage content in the context of input query.
Self-explaining deep models with logic rule reasoning
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Warning: this paper contains model outputs exhibiting offensiveness and biases.
Out-of-Distribution Representation Learning for Time Series Classification
Time series classification is an important problem in real world.
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
For optimization, we utilize an adapted Mixup to generate an out-of-distribution dataset that can guide the preference direction and optimize with Pareto optimization.
Domain-Specific Risk Minimization for Out-of-Distribution Generalization
Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement.
Geometric Interaction Augmented Graph Collaborative Filtering
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.
Domain-invariant Feature Exploration for Domain Generalization
Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i. e., the property within a domain, which is agnostic to other domains.
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
This paper presents FedX, an unsupervised federated learning framework.
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation
The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks.
Cooperative Retriever and Ranker in Deep Recommenders
Deep recommender systems (DRS) are intensively applied in modern web services.
Two-Stage Neural Contextual Bandits for Personalised News Recommendation
We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion.
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions.
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare.
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data.
A Neural Corpus Indexer for Document Retrieval
To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query.
Negative Sampling for Contrastive Representation Learning: A Review
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.
Robust Quantity-Aware Aggregation for Federated Learning
Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework.
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
We first extract distribution patterns from the item candidates.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected.
ProFairRec: Provider Fairness-aware News Recommendation
To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data.
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
We perform comprehensive explorations for the optimal conduct of knowledge distillation, which may provide useful insights for the learning of VQ based ANN index.
A Mutually Reinforced Framework for Pretrained Sentence Embeddings
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.
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
In this paper, we propose a very simple yet effective method named NoisyTune to help better finetune PLMs on downstream tasks by adding some noise to the parameters of PLMs before fine-tuning.
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
In this way, all the clients can participate in the model learning in FL, and the final model can be big and powerful enough.
HousE: Knowledge Graph Embedding with Householder Parameterization
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.
UA-FedRec: Untargeted Attack on Federated News Recommendation
Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus.
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction
To solve the game, we propose a platform negotiation method that simulates the bargaining among platforms and locally optimizes their policies via gradient descent.
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
However, existing general FL poisoning methods for degrading model performance are either ineffective or not concealed in poisoning federated recommender systems.
Reinforcement Routing on Proximity Graph for Efficient Recommendation
Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
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.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
Gophormer: Ego-Graph Transformer for Node Classification
Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
Then we propose a hierarchical CB (HCB) algorithm to explore users' interest in the hierarchy tree.
Towards Fine-Grained Reasoning for Fake News Detection
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients.
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way.
UserBERT: Contrastive User Model Pre-training
Two self-supervision tasks are incorporated in UserBERT for user model pre-training on unlabeled user behavior data to empower user modeling.
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Instead of directly communicating the large models between clients and server, we propose an adaptive mutual distillation framework to reciprocally learn a student and a teacher model on each client, where only the student model is shared by different clients and updated collaboratively to reduce the communication cost.
Fastformer: Additive Attention Can Be All You Need
In this way, Fastformer can achieve effective context modeling with linear complexity.
Ranked #1 on
News Recommendation
on MIND
(using extra training data)
Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer
We then sample token pairs based on their probability scores derived from the sketched attention matrix to generate different sparse attention index matrices for different attention heads.
Localized Graph Collaborative Filtering
These methods often make recommendations based on the learned user and item embeddings.
PENS: A Dataset and Generic Framework for Personalized News Headline Generation
To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS).
Personalized News Recommendation: Methods and Challenges
Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges.
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news.
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
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.
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
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
In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.
DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation
The core of our method includes a bias representation module, a bias-aware user modeling module, and a bias-aware click prediction module.
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention.
Training Large-Scale News Recommenders with Pretrained Language Models in the Loop
Secondly, it improves the data efficiency of the training workflow, where non-informative data can be eliminated from encoding.
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
To incorporate high-order user-item interactions, we propose a user-item graph expansion method that can find neighboring users with co-interacted items and exchange their embeddings for expanding the local user-item graphs in a privacy-preserving way.
Neural News Recommendation with Negative Feedback
The dwell time of news reading is an important clue for user interest modeling, since short reading dwell time usually indicates low and even negative interest.
Real-time Spatio-temporal Action Localization via Learning Motion Representation
In this paper, we exploit better ways to use motion information in a unified end-to-end trainable network architecture.
Fake News Detection through Graph Comment Advanced Learning
Yet, the popularity of social media also provides opportunities to better detect fake news.
Fake News Detection
Representation Learning
Social and Information Networks
Sampling-Decomposable Generative Adversarial Recommender
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.
Self-supervised Graph Learning for Recommendation
In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.
PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data.
FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data
On each platform a local user model is used to learn user embeddings from the local user behaviors on that platform.
MIND: A Large-scale Dataset for News Recommendation
News recommendation is an important technique for personalized news service.
Fine-grained Interest Matching for Neural News Recommendation
Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation.
Graph Neural News Recommendation with Unsupervised Preference Disentanglement
Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability.
FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning
In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes.
Lightrec: A memory and search-efficient recommender system
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.
Privacy-Preserving News Recommendation Model Learning
Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning
Since the labeled data in different platforms usually has some differences in entity type and annotation criteria, instead of constraining different platforms to share the same model, we decompose the medical NER model in each platform into a shared module and a private module.
A Survey on Knowledge Graph-Based Recommender Systems
On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.
Graph Convolution Machine for Context-aware Recommender System
The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.
Neural News Recommendation with Multi-Head Self-Attention
The core of our approach is a news encoder and a user encoder.
Neural News Recommendation with Heterogeneous User Behavior
In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages.
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network
In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews.
KRED: Knowledge-Aware Document Representation for News Recommendations
News articles usually contain knowledge entities such as celebrities or organizations.
A Novel User Representation Paradigm for Making Personalized Candidate Retrieval
Our work is undergoing a anonymous review, and it will soon be released after the notification.
NPA: Neural News Recommendation with Personalized Attention
Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles.
Neural News Recommendation with Attentive Multi-View Learning
In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning.
Ranked #6 on
News Recommendation
on MIND
Neural News Recommendation with Topic-Aware News Representation
The core of our approach is a topic-aware news encoder and a user encoder.
Neural News Recommendation with Long- and Short-term User Representations
In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations.
Ranked #7 on
News Recommendation
on MIND
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction
Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem.
Ranked #1 on
Term Extraction
on SemEval 2014 Task 4 Laptop
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes.
Collaborative Translational Metric Learning
We also conduct extensive qualitative evaluations on the translation vectors learned by our proposed method to ascertain the benefit of adopting the translation mechanism for implicit feedback-based recommendations.
Ranked #1 on
Recommendation Systems
on Declicious
Personalized Multimedia Item and Key Frame Recommendation
We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.
NRPA: Neural Recommendation with Personalized Attention
In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews.
Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors
In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews.
Transcribing Content from Structural Images with Spotlight Mechanism
Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly.
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation
Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming.
Chinese Named Entity Recognition
named-entity-recognition
+1
Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization
Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS.
Knowledge Graph Convolutional Networks for Recommender Systems
To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information.
Ranked #1 on
Click-Through Rate Prediction
on Book-Crossing
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.
Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
User modeling is an essential task for online rec- ommender systems.
Ranked #2 on
Recommendation Systems
on Amazon Product Data
Session-based Recommendation with Graph Neural Networks
To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
Ranked #1 on
Session-Based Recommendations
on Gowalla
Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention
This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions.
Image Inspired Poetry Generation in XiaoIce
This is one of the few attempts to generate poetry from images.
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation
Recent years have witnessed a surge of interest on response generation for neural conversation systems.
MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games
In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games.
Neural Chinese Word Segmentation with Dictionary Knowledge
The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.
A Hierarchical Attention Model for Social Contextual Image Recommendation
After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.
Ranked #1 on
Click-Through Rate Prediction
on Dianping
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.
Ranked #2 on
Click-Through Rate Prediction
on Book-Crossing
DKN: Deep Knowledge-Aware Network for News Recommendation
To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.
Ranked #5 on
News Recommendation
on MIND
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method.
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.
Ranked #1 on
Node Classification
on Wikipedia
A World of Difference: Divergent Word Interpretations among People
We propose an approach that quantifies semantic differences in interpretations among different groups of people.
T-Drive: Driving Directions Based on Taxi Trajectories
GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge.
