Supplementing Product Reviews: Retrieving Opinions from Products with Similar Attributes

Marino Fujii, Takehiro Yamamoto, Takayuki Yumoto

Abstract: In this study, we propose a method to help understand products that don’t have many reviews. Specifically, we use an LLM to retrieve reviews of similar products. This helps users get more useful information when they are thinking of buying a product. First, when the user inputs a product and a question, the system uses the LLM to find the attributes that are related to the user’s question. Next, the system calculates the similarity of the attributes and find similar products. After that, by using an LLM, the system retrieves opinions from reviews of the similar products that are related to these attributes. Finally, the system ranks the opinions that were judged to be related to the attribute queries and shows them as the search results. This adds more useful information to support the target product. In the experiment, we compared the similarity between the opinions retrieved by the proposed method and the actual opinions that the target product has. As a result, opinions retrieved from similar products were slightly more similar to the actual opinions than those from randomly selected products.

External IDs:dblp:conf/iiwas/FujiiYY25