Architecture and Hardware Mobile commerce opportunities and challenges

Information Requirement Elicitation in Mobile Commerce

Using an interactive approach to facilitate information searching for mobile users.
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A key obstacle to m-commerce is how to efficiently allow users to get their desired information at the right time and in the right place. For the immediately foreseeable future, the wireless devices typically used in m-commerce will be limited in input and output capability. These limitations render searches for supplier information inconvenient. One solution is to push information to users, but many consider such information irrelevant and annoying.

Another solution is to provide information according to users’ requirements. However, users usually do not have a sense of their complete information requirements. For example, someone asking for directions often doesn’t know which modes of transportation are available and may not have thought about which form of transportation is preferred. However, the inquiree may determine what options are available and ask the inquirer to choose one so that specific directions can be given. In this example, the inquiree elicits the inquirer’s information requirement with a choice prompt adapted to the inquirer’s need (destination) and context (current location). Similarly, Information Requirement Elicitation (IRE) is the interactive communication in which information systems help users specify their requirements with adaptive choice prompts.

In m-commerce, users are mobile and suppliers are distributed, which makes it difficult to maintain all user and supplier data on one site.

In m-commerce, user need concerns what general products/services a user wants and user information requirement concerns when and where a user may want products/services from which supplier. Users initiate IRE sessions by expressing their needs. To facilitate, m-commerce providers can compile a hotlink inventory on their Web sites. Users can select such links as “directions” and “restaurants” to personalize their wireless devices and specify their initial preference, for example, on food types and price range. When users click the links their devices send the requests, along with the context information, to IRE-enabled systems.

User context is the user-sensible information related to consumer behavior, including the location, surroundings, and physiological information. Some context information can be gathered with wearable sensors [3, 4] and utilized through context-aware computing [1, 2]. Because a user’s context is time- and location-specific, m-commerce is unique in its context-aware capability compared with e-commerce. With users’ context information, IRE-enabled systems can filter suppliers by their contextual relevancy to users. The most basic context information in m-commerce is users’ location, which is directly related to the “where” element of user information requirement. Some domains may require other types of context information; for example, a mobile health care service needs to obtain physiological information, such as a user’s heart rate.

IRE-enabled systems should also utilize user preference information to narrow down options by their personal relevancy. User preference is a user’s tendency toward certain alternative(s) among others. Users with similar experiences are likely to have similar preferences, and once a preference is formed, it is often relatively stable. Hence, a guess about user preference can be made in IRE by finding proximate experience, matching user profiles, or comparing choice frequency.

In m-commerce, users are mobile and suppliers are distributed, and it is difficult to maintain all user and supplier data on one site. Local IRE-enabled systems need to be linked together into a global peer-to-peer distributed database system. Also, new distributed component architectures, such as the Microsoft .Net framework, can lower barriers of data exchange among users, systems, and suppliers.

In each IRE-enabled system, there is an IRE component to handle IRE sessions. Each time a pending request triggers the IRE component, it checks whether the information requirement is specific enough. If it is not, the component generates choice prompts for the missing elements. This loop continues until specific supplier information can be provided (see Figure 1). The system also includes components for Requirement Registration, Data Mining and Data Collection, and databases for Session Data, User History, User Profile, User Preference, and Supplier Data. The Requirement Registration component registers users’ initial requests and responses into Session Data as pending requests, which trigger the IRE component to generate choice prompts based on User Context, User Preference, and Supplier Data. User Context, stored as part of Session Data, is gathered by user-side Context Sensors. User Preference is inferred from User History and User Profile through the Data Mining component. When a session is completed, it becomes part of User History (see Figure 2).

Detailed algorithms differ across domains. For example, to find a restaurant, food type and price are of personal relevancy to users, and distance and opening time are of contextual relevancy. Choice prompts can give options of personal elements that a user has clear preferences, such as food type, along with relevant contextual information, such as distance.

To illustrate the operation of an IRE-enabled system as conceptually designed, consider a scenario in which a user of a GPS-enabled mobile phone is traveling in a city and is seeking a restaurant. The user clicks the “restaurants” link and the phone sends the request along with the user’s location to a local IRE-enabled system and initiates a session. First, the system tries to obtain the user’s “context-specific” preference information from his or her local experience. For example, if the user had dinner in a nearby restaurant “ABC” during the last trip, he or she may want to go there again. The system can generate such a prompt: “You had a dinner in “ABC” two months ago, do you want to go there? 1. Yes; 2. No.” Otherwise, a context-independent preference that is less subject to context change will be retrieved from the site where the user is registered. For example, if the user has an obvious food type preference, the system can locate nearby relevant restaurants and generate this prompt: “These restaurants provide: Food A: 1. Restaurant “ABC” (200 feet); 2. Restaurant “DEF” (1 mile); Food B: 3. Restaurant “XYZ” (500 feet); 4. More choices.” When the user chooses one restaurant, the system will give specific service information, such as menu, directions, and order form.

IRE is a context-aware, personalized, and interactive service to facilitate information search for mobile users. In order to effectively implement IRE in m-commerce, further technical and behavioral issues must be addressed. The IRE-enabled system design, implementation, and operation may greatly improve the service quality of m-commerce providers.

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F1 Figure 1. The logic operation of an IRE component.

F2 Figure 2. The system architecture design of an IRE-enabled information system.

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    1. Dey, A.K., Abowd, G.D., and Salber, D. A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction 16, 2–4 (Dec. 2001), 97–166.

    2. Mandato, D., Kovacs, E., Hohl, F., and Amir-Alikhani, H. CAMP: A context-aware mobile portal. IEEE Communication Magazine 40, 1 (Jan. 2002), 90–97.

    3. Starner, T., Schiele, B. and Pentland, A. Visual contextual awareness in wearable computing. In Proceedings of the 2nd International Symposium on Wearable Computers (ISWC 98), IEEE CS Press, Los Alamitos, Calif., 50–57.

    4. Van Laerhoven, K. and Cakmakci, O. What shall we teach our pants? In Proceedings of the 4th International Symposium on Wearable Computers (ISWC '00), IEEE CS Press, Los Alamitos, Calif., 77–83.

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