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SRS

Sticker Response Selector

Natural Language ProcessingIntroduced 200045 papers

Description

Sticker Response Selector, or SRS, is a model for multi-turn dialog that automatically selects a sticker response. SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score.

Papers Using This Method

CloneShield: A Framework for Universal Perturbation Against Zero-Shot Voice Cloning2025-05-25 HyperKING: Quantum-Classical Generative Adversarial Networks for Hyperspectral Image Restoration2025-04-16 Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation2025-04-09 Filtering with Time-frequency Analysis: An Adaptive and Lightweight Model for Sequential Recommender Systems Based on Discrete Wavelet Transform2025-03-30 What was Said, What was not Said2025-03-18 LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation2025-03-16 System Test Case Design from Requirements Specifications: Insights and Challenges of Using ChatGPT2024-12-04 Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning2024-11-07 LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems2024-11-03 The Role of Fake Users in Sequential Recommender Systems2024-10-13 LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation2024-09-30 SIGMA: Selective Gated Mamba for Sequential Recommendation2024-08-21 Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation2024-08-14 A Reproducible Analysis of Sequential Recommender Systems2024-08-07 Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning2024-06-11 GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems2024-06-06 A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation2024-06-01 LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation2024-05-31 Positional encoding is not the same as context: A study on positional encoding for sequential recommendation2024-05-16 Using LLMs in Software Requirements Specifications: An Empirical Evaluation2024-04-27