PARROT: An Adaptive Online Shopping Guidance System

With the development of e-commerce, it is necessary to build an online shopping guidance system to help users to choose the products they desired. Task-oriented dialogue systems can be used as an online shopping guidance system in e-commerce websites. Current dialogue systems can only extract basic attributes which are the inherent attributes of products. These systems can not process users' requests containing high level attributes which describe products' functions and user experience. These requests, however, appear frequently in real scenarios. To solve this problem, we build PARROT, an adaptive online shopping guidance system. PARROT can extract both basic and high level attributes from dialogues and recommend suitable products to users. The novel features of PARROT are as follows: (1) We propose a new architecture of taskoriented dialogue systems which can extract both basic and high level products' attributes (functional attributes and experience attributes). (2) We construct knowledge base to map from high level attributes to basic level attributes or products. (3) We build a task oriented dialogue system which can finish the task of shopping guidance in websites. We test PARROT in three main scenarios and these tests demonstrate that PARROT can successfully recommend suitable products to users by extracting both basic and high level attributes.