Michelle Brachman, Christopher Bygrave, et al.
AAAI 2022
Understanding customer requirements is a key success factor for both business-to-consumer (B2C) and business-to-business (B2B) enterprises. In a B2C context, most requirements are directly related to products and therefore expressed in keyword-based queries. In comparison, B2B requirements contain more information about customer needs and as such the queries are often in a longer form, ranging from sentences to paragraphs. Such long-form queries pose significant challenges to the information retrieval task in B2B context. In this work, we address the long-form information retrieval challenges by proposing a combination of (i) traditional retrieval methods, to leverage the lexical match from the query, and (ii) state-of-the-art sentence transformers, to capture the rich context in the long queries. We compare our method against traditional TF-IDF and BM25 models on an internal dataset of 12,368 pairs of long-form requirements and products sold. The evaluation shows promising results and provides directions for future work.
Michelle Brachman, Christopher Bygrave, et al.
AAAI 2022
Neil Thompson, Martin Fleming, et al.
IAAI 2024
Vladimir Lipets, Alexander Zadorojniy
MTCSPTA 2021
Ziqi Zhang, Anna Lisa Gentile, et al.
LREC 2010