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Found 30 papers, 16 papers with code
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''.
A Survey on Side Information-driven Session-based Recommendation: From a Data-centric Perspective
In this survey, we provide a comprehensive review of this task from a data-centric perspective.
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
Constructing reasonable positive sample pairs is crucial for the success of contrastive learning.
Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models
Notably, when combined with existing merging techniques, our method enables merged models to outperform specialized fine-tuned models, particularly in code generation tasks.
OPTISHEAR: Towards Efficient and Adaptive Pruning of Large Language Models via Evolutionary Optimization
Post-training pruning has emerged as a crucial optimization technique as large language models (LLMs) continue to grow rapidly.
1bit-Merging: Dynamic Quantized Merging for Large Language Models
Recent advances in large language models have led to specialized models excelling in specific domains, creating a need for efficient model merging techniques.
Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
User behavior records serve as the foundation for recommender systems.
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks
The widespread deployment of pre-trained language models (PLMs) has exposed them to textual backdoor attacks, particularly those planted during the pre-training stage.
NILE: Internal Consistency Alignment in Large Language Models
However, existing IFT datasets often contain knowledge that is inconsistent with LLMs' internal knowledge learned from the pre-training phase, which can greatly affect the efficacy of IFT.
Interpretable Triplet Importance for Personalized Ranking
Personalized item ranking has been a crucial component contributing to the performance of recommender systems.
Mitigating Large Language Model Hallucination with Faithful Finetuning
Large language models (LLMs) have demonstrated remarkable performance on various natural language processing tasks.
Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining
Large Language Models (LLMs) have shown human-like reasoning abilities but still face challenges in solving complex logical problems.
Privacy in LLM-based Recommendation: Recent Advances and Future Directions
Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance.
Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts.
Diffusion-based Contrastive Learning for Sequential Recommendation
Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space.
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning.
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.
Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems.
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.
Offline Imitation Learning with Variational Counterfactual Reasoning
We theoretically analyze the influence of the generated expert data and the improvement of generalization.
Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks
However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes.
3D Semantic Segmentation
Few-shot 3D semantic segmentation
+1
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.
Mutually Guided Few-shot Learning for Relational Triple Extraction
Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations.
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks.
Dynamically Expandable Graph Convolution for Streaming Recommendation
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning
Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
Ranked #2 on
3D Open-Vocabulary Instance Segmentation
on STPLS3D
