Search Results for author:
Found 12 papers, 5 papers with code
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation
Meanwhile, we leverage the LLM recommender as a supplemental component (discarded in deployment) to better capture underlying user preferences from heterogeneous interaction behaviors.
Self context-aware emotion perception on human-robot interaction
In the multimodal, accuracy has experienced an elevation from 77. 48% to 78. 93%.
INeAT: Iterative Neural Adaptive Tomography
Here, we propose a neural rendering method for CT reconstruction, named Iterative Neural Adaptive Tomography (INeAT), which incorporates iterative posture optimization to effectively counteract the influence of posture perturbations in data, particularly in cases involving significant posture variations.
PC-bzip2: a phase-space continuity enhanced lossless compression algorithm for light field microscopy data
Moreover, by introducing the temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.
Large Language Models are Zero-Shot Rankers for Recommender Systems
Recently, large language models (LLMs) (e. g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.
Preparing the Future for Continual Semantic Segmentation
In this study, we focus on Continual Semantic Segmentation (CSS) and present a novel approach to tackle the issue of existing methods struggling to learn new classes.
Towards Effective Instance Discrimination Contrastive Loss for Unsupervised Domain Adaptation
We further propose a target-dominated cross-domain mixup that can incorporate accurate semantic information from the source domain.
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e. g., click, like and purchase).
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
