๐ค AI Summary
ๆฌๆๆๅบไธ็งๆกๆถ๏ผ้่ฟ้ๅค้ๆ ท่ฏไผฐๅคงๅ่ฏญ่จๆจกๅๅจๆ ๆ็กฎๅ้้ๆ
ๅตไธ็ๅ็ๆจ่ๅๆๅ้ฎ้ข๏ผไฝฟ็จBRP@kๅMRR@k่กก้ใ
๐ Abstract
Large language models (LLMs) are increasingly used for product recommendation, but evaluating their recommendations presents challenges that differ from conventional information retrieval and recommender systems. LLMs can generate recommendations without an explicit candidate set, and repeated responses to the same query can produce different brands and rankings. We introduce a framework for evaluating open-ended LLM brand recommendations that defines the competitive set independently of model outputs and estimates recommendation prevalence and prominence through repeated sampling. We operationalize these constructs using Brand Recommendation Probability (BRP@$k$) and Mean Reciprocal Rank (MRR@$k$), and apply the framework to six LLMs across five product categories. Category-only queries reveal substantial omission of established brands and limited evidence that recommendation prominence follows conventional brand popularity. Instead, prominence is associated with broader marketplace-visibility signals, particularly search interest and online brand conversation. Needs-based queries show that contextualizing users' goals and constraints changes which brands are retrieved, while diagnostic positioning probes demonstrate that brands omitted from ordinary recommendations can remain conditionally retrievable when distinctive cues are supplied. These findings highlight the need to evaluate LLM recommendation as a stochastic retrieval-and-ranking process rather than from individual generated lists. We provide open-source software and data to support reproducible evaluation of LLM-generated brand recommendations.