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Found 169 papers, 115 papers with code
Graph-based Aspect Representation Learning for Entity Resolution
Entity Resolution (ER) identifies records that refer to the same real-world entity.
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
Neural discrete reasoning (NDR) has shown remarkable progress in combining deep models with discrete reasoning.
Exact and Efficient Unlearning for Large Language Model-based Recommendation
The evolving paradigm of Large Language Model-based Recom- mendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommenda- tion data.
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications.
Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients
Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information.
Boosting Few-Shot Learning via Attentive Feature Regularization
Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor.
Proactive Recommendation with Iterative Preference Guidance
However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization.
The 2nd Workshop on Recommendation with Generative Models
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations.
Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning
To achieve this, we propose a Bi-level Learnable LLM Planner framework, which combines macro-learning and micro-learning through a hierarchical mechanism.
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
Compared to AUC, LLPAUC considers only the partial area under the ROC curve in the Lower-Left corner to push the optimization focus on Top-K. We provide theoretical validation of the correlation between LLPAUC and Top-K ranking metrics and demonstrate its robustness to noisy user feedback.
DiFashion: Towards Personalized Outfit Generation and Recommendation
The evolution of Outfit Recommendation (OR) in the realm of fashion has progressed through two distinct phases: Pre-defined Outfit Recommendation and Personalized Outfit Composition.
Item-side Fairness of Large Language Model-based Recommendation System
Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS.
EXGC: Bridging Efficiency and Explainability in Graph Condensation
Graph representation learning on vast datasets, like web data, has made significant strides.
Towards 3D Molecule-Text Interpretation in Language Models
Through 3D molecule-text alignment and 3D molecule-centric instruction tuning, 3D-MoLM establishes an integration of 3D molecular encoder and LM.
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes -- abnormal nodes are a minority, therefore holding high heterophily and low homophily compared to normal nodes.
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Rather than directly dismissing the role of incremental learning, we ascribe this lack of anticipated performance improvement to the mismatch between the LLM4Recarchitecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendation, hampering its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context.
CAR: Consolidation, Augmentation and Regulation for Recipe Retrieval
Learning recipe and food image representation in common embedding space is non-trivial but crucial for cross-modal recipe retrieval.
LLaRA: Large Language-Recommendation Assistant
Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space.
Large Language Model Can Interpret Latent Space of Sequential Recommender
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items.
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence.
CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation.
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders. Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender.
Attack Prompt Generation for Red Teaming and Defending Large Language Models
Furthermore, we propose a defense framework that fine-tunes victim LLMs through iterative interactions with the attack framework to enhance their safety against red teaming attacks.
A Content-Driven Micro-Video Recommendation Dataset at Scale
Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries.
Text-to-Image Generation for Abstract Concepts
Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts.
Selective Volume Mixup for Video Action Recognition
In this paper, we propose a novel video augmentation strategy named Selective Volume Mixup (SV-Mix) to improve the generalization ability of deep models with limited training videos.
RecAD: Towards A Unified Library for Recommender Attack and Defense
Despite significant research progress in recommender attack and defense, there is a lack of a widely-recognized benchmarking standard in the field, leading to unfair performance comparison and limited credibility of experiments.
CgT-GAN: CLIP-guided Text GAN for Image Captioning
Particularly, we use adversarial training to teach CgT-GAN to mimic the phrases of an external text corpus and CLIP-based reward to provide semantic guidance.
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction.
Recommendation Unlearning via Influence Function
In this work, we propose an Influence Function-based Recommendation Unlearning (IFRU) framework, which efficiently updates the model without retraining by estimating the influence of the unusable data on the model via the influence function.
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i. e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential.
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM).
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples.
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
However, such a manner inevitably learns unstable feature interactions, i. e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving.
Diffusion Recommender Model
In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner.
Generative Recommendation: Towards Next-generation Recommender Paradigm
However, such a retrieval-based recommender paradigm faces two limitations: 1) the human-generated items in the corpus might fail to satisfy the users' diverse information needs, and 2) users usually adjust the recommendations via inefficient passive feedback, e. g., clicks.
GIF: A General Graph Unlearning Strategy via Influence Function
Then, we recognize the crux to the inability of traditional influence function for graph unlearning, and devise Graph Influence Function (GIF), a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $\epsilon$-mass perturbation in deleted data.
Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match.
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
In this paper, we propose Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs.
Adap-$τ$: Adaptively Modulating Embedding Magnitude for Recommendation
Recent years have witnessed the great successes of embedding-based methods in recommender systems.
Weakly Supervised Anomaly Detection: A Survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Specifically, they suffer from two main limitations: 1) existing Graph Convolutional Network (GCN) methods in hyperbolic space rely on tangent space approximation, which would incur approximation error in representation learning, and 2) due to the lack of inner product operation definition in hyperbolic space, existing methods can only measure the plausibility of facts (links) with hyperbolic distance, which is difficult to capture complex data patterns.
On the Theories Behind Hard Negative Sampling for Recommendation
Secondly, we prove that OPAUC has a stronger connection with Top-K evaluation metrics than AUC and verify it with simulation experiments.
Unbiased Knowledge Distillation for Recommendation
Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model.
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task
In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).
Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation
In this work, we propose to model multi-grained popularity features and jointly learn them together with high-order connectivity, to match the differentiation of user preferences exhibited in popularity features.
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions.
Causal Inference in Recommender Systems: A Survey and Future Directions
Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
To facilitate model learning, we further collect rich features of users and items as well as users' behavior history.
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.
Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network
On the one hand, their performance will dramatically degrade along with the increasing sparsity of KGs.
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages.
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.
Differentiable Invariant Causal Discovery
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
However, existing graph-based methods fails to consider the bias offsets of users (items).
Mitigating Hidden Confounding Effects for Causal Recommendation
Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback.
Addressing Confounding Feature Issue for Causal Recommendation
We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost.
Cross Pairwise Ranking for Unbiased Item Recommendation
In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.
Reinforced Causal Explainer for Graph Neural Networks
Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.
Attention in Attention: Modeling Context Correlation for Efficient Video Classification
Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts.
Rumor Detection with Self-supervised Learning on Texts and Social Graph
At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.
Group Contextualization for Video Recognition
By utilizing calibrators to embed feature with four different kinds of contexts in parallel, the learnt representation is expected to be more resilient to diverse types of activities.
Ranked #3 on
Egocentric Activity Recognition
on EGTEA
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.
IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search
On this basis, we develop a specific interactive hypergraph neural network to explicitly encode the structure information (i. e., collaborative signal) into the embedding process.
Discovering Invariant Rationales for Graph Neural Networks
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.
Deconfounding to Explanation Evaluation in Graph Neural Networks
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
Causal Attention for Interpretable and Generalizable Graph Classification
To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.
Learning Robust Recommender from Noisy Implicit Feedback
Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes the noisy interactions by two paradigms (i. e., Truncated Loss and Reweighted Loss).
Towards Multi-Grained Explainability for Graph Neural Networks
A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.
Inductive Lottery Ticket Learning for Graph Neural Networks
In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity.
Knowledge Graph Completion as Tensor Decomposition: A Genreal Form and Tensor N-rank Regularization
Based on the general form, we show the principles of model design to satisfy logical rules.
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems.
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity.
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks.
Causal Incremental Graph Convolution for Recommender System Retraining
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.
Time-aware Path Reasoning on Knowledge Graph for Recommendation
In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations.
Exploring Lottery Ticket Hypothesis in Media Recommender Systems
We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model.
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain.
Empowering Language Understanding with Counterfactual Reasoning
Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning.
Deconfounded Recommendation for Alleviating Bias Amplification
In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounder effect of imbalanced item distribution on user representation and prediction score.
Learning Robust Recommenders through Cross-Model Agreement
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
Graph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Causal Intervention for Leveraging Popularity Bias in Recommendation
This work studies an unexplored problem in recommendation -- how to leverage popularity bias to improve the recommendation accuracy.
AutoDebias: Learning to Debias for Recommendation
This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.
Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.
Learning Intents behind Interactions with Knowledge Graph for Recommendation
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
Advances and Challenges in Conversational Recommender Systems: A Survey
In this paper, we provide a systematic review of the techniques used in current CRSs.
Causal Screening to Interpret Graph Neural Networks
In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.
On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner
In this paper, we model the author disambiguation as a collaboration network reconstruction problem, and propose an incremental and unsupervised author disambiguation method, namely IUAD, which performs in a bottom-up manner.
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Existing work addresses this issue with Inverse Propensity Weighting (IPW), which decreases the impact of popular items on the training and increases the impact of long-tail items.
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning.
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction.
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.
Bias and Debias in Recommender System: A Survey and Future Directions
This motivates us to provide a systematic survey of existing work on RS biases.
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
However, we argue that there is a significant gap between clicks and user satisfaction -- it is common that a user is "cheated" to click an item by the attractive title/cover of the item.
CatGCN: Graph Convolutional Networks with Categorical Node Features
In this paper, we propose a new GCN model named CatGCN, which is tailored for graph learning when the node features are categorical.
Adversarial Attack on Large Scale Graph
Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance.
A Survey on Large-scale Machine Learning
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.
Disentangled Graph Collaborative Filtering
Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.
Interactive Path Reasoning on Graph for Conversational Recommendation
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference.
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
In this work, we introduce a new understanding for it -- data augmentation, which is more transparent than the previous understandings.
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
Denoising Implicit Feedback for Recommendation
In this work, we explore the central theme of denoising implicit feedback for recommender training.
Modeling Personalized Item Frequency Information for Next-basket Recommendation
NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items.
Ranked #1 on
Next-basket recommendation
on TaFeng
How to Retrain Recommender System? A Sequential Meta-Learning Method
Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation
Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities. Distinct from other scenarios (e. g., social networking or content sharing) which recommend a single item (e. g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference.
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.
Bundle Recommendation with Graph Convolutional Networks
Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles.
Graph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Modelling High-Order Social Relations for Item Recommendation
The prevalence of online social network makes it compulsory to study how social relations affect user choice.
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Properly handling missing data is a fundamental challenge in recommendation.
A Survey of Adversarial Learning on Graphs
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
Price-aware Recommendation with Graph Convolutional Networks
Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny.
Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions
Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data.
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models.
Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network
Given a set of symptoms to treat, we aim to generate an overall syndrome representation by effectively fusing the embeddings of all the symptoms in the set, to mimic how a doctor induces the syndromes.
Bilinear Graph Neural Network with Neighbor Interactions
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
Ranked #6 on
Collaborative Filtering
on Gowalla
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.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks.
Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction
Next, we define user's attributes as two categories: spatial attributes (e. g., social role of user) and temporal attributes (e. g., post content of user).
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities.
Ranked #1 on
Multi-Media Recommendation
on MovieLens 10M
Improving Neural Relation Extraction with Implicit Mutual Relations
In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible.
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF.
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it.
Learning to Compose and Reason with Language Tree Structures for Visual Grounding
Grounding natural language in images, such as localizing "the black dog on the left of the tree", is one of the core problems in artificial intelligence, as it needs to comprehend the fine-grained and compositional language space.
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models.
Neural Graph Collaborative Filtering
Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
Ranked #6 on
Link Prediction
on MovieLens 25M
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
Ranked #2 on
Link Prediction
on Yelp
Visually-aware Recommendation with Aesthetic Features
While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization.
Ranked #3 on
Node Classification
on NELL
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
In this paper, we jointly learn the model of recommendation and knowledge graph completion.
Ranked #1 on
Knowledge Graph Completion
on MovieLens 1M
Learning Vertex Representations for Bipartite Networks
Recent years have witnessed a widespread increase of interest in network representation learning (NRL).
Counterfactual Critic Multi-Agent Training for Scene Graph Generation
CMAT is a multi-agent policy gradient method that frames objects as cooperative agents, and then directly maximizes a graph-level metric as the reward.
Explainable Reasoning over Knowledge Graphs for Recommendation
Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest.
Deep Item-based Collaborative Filtering for Top-N Recommendation
In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items.
Attentive Aspect Modeling for Review-aware Recommendation
The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product.
Fast Matrix Factorization with Non-Uniform Weights on Missing Data
This poses an imbalanced learning problem, since the scale of missing entries is usually much larger than that of observed entries, but they cannot be ignored due to the valuable negative signal.
Enhancing Stock Movement Prediction with Adversarial Training
The key novelty is that we propose to employ adversarial training to improve the generalization of a neural network prediction model.
Generative Adversarial Active Learning for Unsupervised Outlier Detection
In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.
Temporal Relational Ranking for Stock Prediction
Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
Learning to Recommend with Multiple Cascading Behaviors
To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.
NAIS: Neural Attentive Item Similarity Model for Recommendation
As such, the key to an item-based CF method is in the estimation of item similarities.
Adversarial Training Towards Robust Multimedia Recommender System
To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.
Information Retrieval Multimedia
Aesthetic-based Clothing Recommendation
Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner.
A Simple Convolutional Generative Network for Next Item Recommendation
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.
Outer Product-based Neural Collaborative Filtering
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.
Adversarial Personalized Ranking for Recommendation
Extensive experiments on three public real-world datasets demonstrate the effectiveness of APR --- by optimizing MF with APR, it outperforms BPR with a relative improvement of 11. 2% on average and achieves state-of-the-art performance for item recommendation.
Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility.
Batch IS NOT Heavy: Learning Word Representations From All Samples
Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations.
Discrete Factorization Machines for Fast Feature-based Recommendation
In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.
Neural Factorization Machines for Sparse Predictive Analytics
However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data.
Ranked #2 on
Link Prediction
on MovieLens 25M
Neural Collaborative Filtering
When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data.
BiRank: Towards Ranking on Bipartite Graphs
In this paper, we study the problem of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector).
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions.
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises.
Item Silk Road: Recommending Items from Information Domains to Social Users
In this work, we address the problem of cross-domain social recommendation, i. e., recommending relevant items of information domains to potential users of social networks.
Ranked #2 on
Recommendation Systems
on WeChat
Attributed Social Network Embedding
For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks.
Social and Information Networks
A Generic Coordinate Descent Framework for Learning from Implicit Feedback
A diversity of complex models has been proposed for a wide variety of applications.
