  The procure to pay process (P2P) in large enterprises is a back-end business
process which deals with the procurement of products and services for
enterprise operations. Procurement is done by issuing purchase orders to
impaneled vendors and invoices submitted by vendors are paid after they go
through a rigorous validation process. Agents orchestrating P2P process often
encounter the problem of matching a product or service descriptions in the
invoice to those in purchase order and verify if the ordered items are what
have been supplied or serviced. For example, the description in the invoice and
purchase order could be TRES 739mL CD KER Smooth and TRES 0.739L CD KER Smth
which look different at word level but refer to the same item. In a typical P2P
process, agents are asked to manually select the products which are similar
before invoices are posted for payment. This step in the business process is
manual, repetitive, cumbersome, and costly. Since descriptions are not
well-formed sentences, we cannot apply existing semantic and syntactic text
similarity approaches directly. In this paper, we present two approaches to
solve the above problem using various types of available agent's recorded
feedback data. If the agent's feedback is in the form of a relative ranking
between descriptions, we use similarity ranking algorithm. If the agent's
feedback is absolute such as match or no-match, we use classification
similarity algorithm. We also present the threats to the validity of our
approach and present a possible remedy making use of product taxonomy and
catalog. We showcase the comparative effectiveness and efficiency of the
proposed approaches over many benchmarks and real-world data sets.
