  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.
