View PDF ↗
PDF Viewer
Open Full PDF

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (22)

[1]
Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting
2025Alexandre Andre, Gauthier Roy et al.
[2]
Large Language Model Enhanced Recommender Systems: A Survey
2024Qidong Liu, Xiangyu Zhao et al.
[3]
On explaining recommendations with Large Language Models: a review
2024Alan Said
[4]
LLMRec: Large Language Models with Graph Augmentation for Recommendation
2023Wei Wei, Xubin Ren et al.
[5]
Evaluating ChatGPT as a Recommender System: A Rigorous Approach
2023Dario Di Palma, Giovanni Maria Biancofiore et al.
[6]
A survey on large language models for recommendation
2023Likang Wu, Zhilan Zheng et al.
[7]
Large Language Models are Zero-Shot Rankers for Recommender Systems
2023Yupeng Hou, Junjie Zhang et al.
[8]
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
2023Keqin Bao, Jizhi Zhang et al.
[9]
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
2023Yunfan Gao, Tao Sheng et al.
[10]
Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity
2023Diana C. Hernandez-Bocanegra, J. Ziegler
[11]
Evaluating Recommender Systems: Survey and Framework
2022Eva Zangerle, Christine Bauer
[12]
A Survey on Trustworthy Recommender Systems
2022Yingqiang Ge, Shuchang Liu et al.
[13]
User Trust in Recommendation Systems: A comparison of Content-Based, Collaborative and Demographic Filtering
2022Mengqi Liao, S. Sundar et al.
[14]
“Technique for the Measurement of Attitudes, A”
2022R. Likert
[15]
Human-Centered Recommender Systems: Origins, Advances, Challenges, and Opportunities
2021J. Konstan, L. Terveen
[16]
A Survey on Conversational Recommender Systems
2020D. Jannach, A. Manzoor et al.
[17]
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
2019Jianmo Ni, Jiacheng Li et al.
[18]
A user-centric evaluation framework for recommender systems
2011P. Pu, Li Chen et al.
[19]
Computing Krippendorff's Alpha-Reliability
2011K. Krippendorff
[20]
Explaining Recommendations
2007N. Tintarev

Showing 20 of 22 references

Founder's Pitch

"HELM is an open-source toolkit for human-centered evaluation of LLM-powered recommender systems in real-world user experiences."

Recommender Systems EvaluationScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 1/27/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.