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Profile Generation
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Profile Generation is the task of creating a profile for an individual or entity based on information about them. The goal of profile generation is to gather as much relevant information about the individual or entity as possible, in order to build an accurate and comprehensive profile.
논문
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LettinGo: Explore User Profile Generation for Recommendation System
Can LLM-Driven Hard Negative Sampling Empower Collaborative Filtering? Findings and Potentials
Automating Personalization: Prompt Optimization for Recommendation Reranking
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
User Profile with Large Language Models: Construction, Updating, and Benchmarking
Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking
A Prompting-Based Representation Learning Method for Recommendation with Large Language Models
Guided Profile Generation Improves Personalization with LLMs
A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
PGTask: Introducing the Task of Profile Generation from Dialogues
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking
Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework
Modeling Generalized Rate-Distortion Functions
Topical Generalization for Presentation of User Profiles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.