“Fixing the curse of the bad product descriptions” – Search-boosted tag recommendation for E-commerce products

Abstract

Various e-commerce platforms allow sellers to register, describe and organize their own products, using tags and other textual metadata. The quality of these textual descriptors is essential for the effectiveness of e-commerce information services such as search and product recommendation, and thus, for the ability of consumers to find desired products. In this paper, we focus on a particular, widely used textual descriptors of products, tags. We argue that sellers may not be the “best” providers of tag information for products either because of their inability to do so (they were not “trained” for that) or due to an explicit intent to fool the system in order to promote their products with inadequate or imprecise tags (tag spam). To deal with these issues, we may rely on automatic tag recommendation techniques to improve the quality of the tags suggested to describe a given product. In this context, the main novel contribution of our work is a set of new tag recommendation techniques that take advantage of product search result data (in particular the search queries and product clicks from these queries) to improve the quality of the recommended tags. Our main hypothesis is that the set of queries collectively issued by the consumers of the e-market place, along with corresponding clicks, reflect a more trustworthy view of the products; thus those queries and clicks can be exploited as a source of high quality (e.g., more diverse) tags to describe the products. We propose new solutions, including some based on deep learning, that translate this main hypothesis into new features and methods for recommending tags for products. Our manual and automatic evaluations, using real data from one of the largest e-commerce sites in Brazil, show that indeed tags created by sellers contain a lot of noise. On the other hand, our proposed search-boosted tag recommenders are highly effective in suggesting relevant tags, with gains of more than 16% in recommendation effectiveness against the state-of-the-art. Even more, our experiments show that the suggested tags provide a potentially better data source for e-commerce search than the original tags assigned by product sellers.