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