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Scott Sanner

5 papers hereh-index 317 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.IR4
  • cs.LG1
same name
  • Scott Sanner — 11 papers, h 8
  • Scott Sanner — 7 papers, h 2
  • Scott Sanner — 7 papers, h 3
  • Scott Sanner — 4 papers, h 3
  • Scott Sanner — 3 papers, h 6
  • Scott Sanner — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.IRShow all

4 papers · 1 filter

cs.IR2026

VOGUE: A Multimodal Dataset for Conversational Recommendation in Fashion

David Guo, Minqi Sun, Yilun Jiang +2

Multimodal conversational recommendation has recently emerged as a promising paradigm for delivering personalized experiences through natural dialogue enriched by visual and contex…

cs.IR2026

Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval

Junyoung Kim, Anton Korikov, Jiazhou Liang +5

While Large Language Models (LLMs) exhibit exceptional zero-shot relevance modeling, their high computational cost necessitates framing passage retrieval as a budget-constrained gl…

cs.IR2025

Multimodal Item Scoring for Natural Language Recommendation via Gaussian Process Regression with LLM Relevance Judgments

Yifan Liu, Qianfeng Wen, Jiazhou Liang +6

Natural Language Recommendation (NLRec) generates item suggestions based on the relevance between user-issued NL requests and NL item description passages. Existing NLRec approache…

cs.IR2025

MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval

Yifan Liu, Qianfeng Wen, Mark Zhao +2

Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query-passage relevance in embedding space, which is effective when embeddings lie on a li…

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