A Conversational Interface for Online Shopping

A Conversational Interface for Online Shopping Joyce Chai, Veronika Horvath, Nanda Kambhatla, Nicolas Nicolov & Margo Stys-Budzikowska Conversational Dialog Systems IBM T. J. Watson Research Center 30 Saw Mill River Rd, Hawthorne, NY 10532, USA {jchai, veronika, nanda, nicolas, sm1}@us.ibm.com ABSTRACT We present a deployed, conversational dialog system that assists users in finding computers based on their usage patterns and constraints on specifications. We discuss findings from a market survey and two user studies. We compared our system to a directed dialog system and a menu driven navigation system. We found that the conversational interface reduced the average number of clicks by 63% and the average interaction time by 33% over a menu driven search system. The focus of our continuing work includes developing a dynamic, adaptive dialog management strategy, robustly handling user input and improving the user interface. 1. INTRODUCTION Conversational interfaces allow users to interact with automated systems using speech or typed in text via "conversational dialog". For the purposes of this paper, a conversational dialog consists of a sequence of interactions between a user and a system. The user input is interpreted in the context of previous user inputs in the current session and from previous sessions. Conversational interfaces offer greater flexibility to users than menu-driven (i.e., directed-dialog) interfaces, where users navigate menus that have a rigid structure [5,4]. Conversational interfaces permit users to ask queries directly in their own words. Thus, users do not have to understand the terminology used by system designers to label hyperlinks on a website or internalize the hierarchical menus of a telephone system [3] or websites. Recently, conversational interfaces for executing simple transactions and for finding information are proliferating [7,6]. In this paper, we present a conversational dialog system, Natural Language Assistant (or NLA), that helps users shop for notebook computers and discuss the results of user studies that we conducted with this system. 2. NATURAL LANGUAGE ASSISTANT NLA assists users in finding notebooks that satisfy their needs by engaging them in a dialog. At each turn of the dialog, NLA provides incremental feedback about its understanding of the user's constraints and shows products that match these constraints. By encouraging iterative refinement of the user's query, the system finds more user constraints and, ultimately, recommends a product that best matches the user's criteria. The system consists of three major modules (cf. Figure 1): Presentation Manager, Dialog Manager, and Action Manager. The Presentation Manager interprets user input and generates system responses. It embodies the user interface and contains a shallow semantic parser and a response generator. The semantic parser identifies concepts (e.g., MULTIMEDIA) and constraints on product attributes (e.g., hard disk size more than 20GB) from the textual user input. The concepts mediate the mapping between user input and available products through product specifications. They implement the business logic. The Dialog Manager uses the current requirements and formulates action plans for the Action Manager to perform back-end operations (e.g., database access1). The Dialog Manager constructs a response to the user based on …

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