Towards Linking Camouflaged Descriptions to Implicit Products in E-commerce

Longtao Huang, Bo Yuan, Rong Zhang, Quan Lu

Abstract: As the emergence of E-commerce services, billions of products are sold online everyday. How to detect illegal products from the large-scale online products has become an important and practical research problem. In order to evade detection, malicious sellers usually utilize camouflaged text to describe their illegal products implicitly. Thus brings great challenges to the current detection systems since newly camouflaged text can hardly be learned from historical data and the distribution of illegal and normal products is extremely unbalanced. Rather than solving this problem as a classification task in most previous efforts, we reformulate the problem from a perspective of implicit entity linking, which targets at linking a camouflaged description to a known product. In this paper, we introduce three types of context that could help to infer implicit entity from camouflaged descriptions and propose an end-to-end contextual representation model to capture the effect of different context. Furthermore, we involve a symmetric metric to model the matching score of the input title to the product by learning the mutual effect among the context. The experimental results on the datasets collected from a real-world E-commerce site demonstrate the advantage of the proposed model against the state-of-the-art methods.