# Multi-Aspect Reviewed-Item Retrieval via LLM Query Decomposition and Aspect Fusion

arXiv: https://arxiv.org/abs/2408.00878 · HTML: https://mlsanity.com/paper/2408.00878/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2024.

## Built on by (most cited first, 5 of 5)

- [RAG-R1: Incentivizing the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism](https://mlsanity.com/paper/2507.02962/) (2025)
- [Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination Recommendation](https://mlsanity.com/paper/2410.01598/) (2024)
- [A Survey of Query Optimization in Large Language Models](https://mlsanity.com/paper/2412.17558/) (2024)
- [Large Language Model Driven Recommendation](https://mlsanity.com/paper/2408.10946/) (2024)
- [ReBOL: Retrieval via Bayesian Optimization with Batched LLM Relevance Observations and Query Reformulation](https://mlsanity.com/paper/2603.20513/) (2026)

## Abstract (arXiv)

While user-generated product reviews often contain large quantities of information, their utility in addressing natural language product queries has been limited, with a key challenge being the need to aggregate information from multiple low-level sources (reviews) to a higher item level during retrieval. Existing methods for reviewed-item retrieval (RIR) typically take a late fusion (LF) approach which computes query-item scores by simply averaging the top-K query-review similarity scores for an item. However, we demonstrate that for multi-aspect queries and multi-aspect items, LF is highly sensitive to the distribution of aspects covered by reviews in terms of aspect frequency and the degree of aspect separation across reviews. To address these LF failures, we propose several novel aspect fusion (AF) strategies which include Large Language Model (LLM) query extraction and generative reranking. Our experiments show that for imbalanced review corpora, AF can improve over LF by a MAP@10 increase from 0.36 to 0.52, while achieving equivalent performance for balanced review corpora.