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Profile Generation

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.

Papers

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Showing 1–18 of 18 papers

TitleStatusHype
Can LLM-Driven Hard Negative Sampling Empower Collaborative Filtering? Findings and PotentialsCode1
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity LinkingCode1
Automating Personalization: Prompt Optimization for Recommendation Reranking—0
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models—0
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System—0
Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework—0
Guided Profile Generation Improves Personalization with LLMs—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking—0
LettinGo: Explore User Profile Generation for Recommendation System—0
Modeling Generalized Rate-Distortion Functions—0
User Profile with Large Language Models: Construction, Updating, and Benchmarking—0
A Prompting-Based Representation Learning Method for Recommendation with Large Language Models—0
Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking—0
Topical Generalization for Presentation of User Profiles—0
PresSim: An End-to-end Framework for Dynamic Ground Pressure Profile Generation from Monocular Videos Using Physics-based 3D Simulation—0
PGTask: Introducing the Task of Profile Generation from DialoguesCode0
A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and PredictionCode0
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