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Paper

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

by Independent / Community 2305.07001

Free2AITools Nexus Index

60.1

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 90

P: Popularity 68

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

In the past few decades, recommender systems have attracted much attention in both research and industry communities. Existing recommendation models mainly learn the underlying user preference from historical behavior data (typically in the forms of item IDs), and then estimate the user–item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we develop a different recommendation paradigm, considering recommendation as instruction follo...

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Semantic Scholar 310 Citations

Paper Information Summary
Entity Passport
Registry ID	2305.07001
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2305_07001,
  author = {Unknown},
  title = {Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2305.07001}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach [Paper]. Free2AITools. https://arxiv.org/abs/2305.07001

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 90

Popularity (P) 68

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach: Authority (A:90), Popularity (P:68), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"In the past few decades, recommender systems have attracted much attention in both research and industry communities. Existing recommendation models mainly learn the underlying user preference from historical behavior data (typically in the forms of item IDs), and then estimate the user–item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we develop a different recommendation paradigm, considering recommendation as instruction follo..."

❝ Cite Node

@article{Zhang2026Recommendation,
  title={Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach},
  author={Junjie Zhang and Ruobing Xie and Yupeng Hou and Wayne Xin Zhao and Leyu Lin and Ji-rong Wen},
  journal={arXiv preprint arXiv:2305.07001},
  year={2026}
}

👥 Collaborating Minds

Junjie Zhang Ruobing Xie Yupeng Hou Wayne Xin Zhao Leyu Lin Ji-rong Wen

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈310CitationsSemantic Scholar

🏛️90AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

🏷️ Research Topics

attention mechanisminstruction tuning

📦Data Source: semantic_scholar

🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: 2305.07001
slug: 2305.07001
source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: 0
forks: 0
citations: 310

Data indexed from public sources. Updated daily.