  The constant growth of the e-commerce industry has rendered the problem of
product retrieval particularly important. As more enterprises move their
activities on the Web, the volume and the diversity of the product-related
information increase quickly. These factors make it difficult for the users to
identify and compare the features of their desired products. Recent studies
proved that the standard similarity metrics cannot effectively identify
identical products, since similar titles often refer to different products and
vice-versa. Other studies employed external data sources (search engines) to
enrich the titles; these solutions are rather impractical mainly because the
external data fetching is slow. In this paper we introduce UPM, an unsupervised
algorithm for matching products by their titles. UPM is independent of any
external sources, since it analyzes the titles and extracts combinations of
words out of them. These combinations are evaluated according to several
criteria, and the most appropriate of them constitutes the cluster where a
product is classified into. UPM is also parameter-free, it avoids product
pairwise comparisons, and includes a post-processing verification stage which
corrects the erroneous matches. The experimental evaluation of UPM demonstrated
its superiority against the state-of-the-art approaches in terms of both
efficiency and effectiveness.
