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Found 22 papers, 4 papers with code
Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
Motivated by these observations, we embarked on an empirical study from the perspective of attention mechanisms to explore how LLMs process graph-structured data.
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs
Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions.
GANPrompt: Enhancing Robustness in LLM-Based Recommendations with GAN-Enhanced Diversity Prompts
In recent years, Large Language Models (LLMs) have demonstrated remarkable proficiency in comprehending and generating natural language, with a growing prevalence in the domain of recommendation systems.
LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction.
Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
Data bias, e. g., popularity impairs the dynamics of two-sided markets within recommender systems.
Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach
Furthermore, in addition to the supervised loss for overlapping users, we design contrastive tasks for non-overlapping users from both group and individual-levels to avoid model skew and enhance the semantics of representations.
Enhancing Collaborative Semantics of Language Model-Driven Recommendations via Graph-Aware Learning
To address this, we consider enhancing the learning capability of language model-driven recommendation models for structured data, specifically by utilizing interaction graphs rich in collaborative semantics.
LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modeling
Specifically, since natural language descriptions are not sufficient for LLMs to understand and process graph-structured data, fine-tuned LLMs perform even worse than some traditional GNN models on graph tasks, lacking inherent modeling capabilities for graph structures.
Multi-View Empowered Structural Graph Wordification for Language Models
Significant efforts have been dedicated to integrating the powerful Large Language Models (LLMs) with diverse modalities, particularly focusing on the fusion of language, vision and audio data.
Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment
One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
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.
Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.
Cross-domain recommendation via user interest alignment
Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains.
Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction
To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i. e., preference topics generation, preference shift modeling, and social influence activation.
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
Usually, this prediction is always with great challenges to making a comprehensive understanding of both the start-up project and market environment.
Learning the Implicit Semantic Representation on Graph-Structured Data
Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole, while the implicit semantic associations behind highly complex interactions of graphs are largely unexploited.
Learning the Compositional Visual Coherence for Complementary Recommendations
Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents.
Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
In the crowdfunding market, the early fundraising performance of the project is a concerned issue for both creators and platforms.
Promotion of Answer Value Measurement with Domain Effects in Community Question Answering Systems
Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multi-facet domain effects in CQA are still underexplored.
Exploiting Cognitive Structure for Adaptive Learning
Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e. g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items.
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations.
