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Found 15 papers, 9 papers with code
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.
Data-efficient Fine-tuning for LLM-based Recommendation
To pursue the two objectives, we propose a novel data pruning method based on two scores, i. e., influence score and effort score, to efficiently identify the influential samples.
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
They learn a feature extractor on warm-start items to align feature representations with interactions, and then leverage the feature extractor to extract the feature representations of cold-start items for interaction prediction.
A Multi-facet Paradigm to Bridge Large Language Model and Recommendation
To combat these issues, we propose a novel multi-facet paradigm, namely TransRec, to bridge the LLMs to recommendation.
Illumination-insensitive Binary Descriptor for Visual Measurement Based on Local Inter-patch Invariance
Existing binary descriptors may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations.
Level-line Guided Edge Drawing for Robust Line Segment Detection
Line segment detection plays a cornerstone role in computer vision tasks.
A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges
The challenges in existing methods and corresponding insights for potentially solving them are also provided to inspire researchers.
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.
Causal Disentangled Recommendation Against User Preference Shifts
Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: 1) capturing the preference shifts across environments for accurate preference prediction, and 2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference.
Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work.
Mitigating Spurious Correlations for Self-supervised Recommendation
This objective requires to 1) automatically mask spurious features without supervision, and 2) block the negative effect transmission from spurious features to other features during SSL.
Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification
Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID.
3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder
Researchers have proposed various methods to extract 3D keypoints from the surface of 3D mesh models over the last decades, but most of them are based on geometric methods, which lack enough flexibility to meet the requirements for various applications.
Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement
Three dimensional (3D) interest point detection plays a fundamental role in 3D computer vision and graphics.
