This use case is served by Agent A4 and involves Type-2 interaction, but it can also be initialized by the user (Type-1 interaction). There are two main scenarios for this use case, depending on the direction of communication.
In the first scenario, the user or his agents request the information that is not available in the system. In this case, Agent A4 initializes a search for other sources of information. These sources include different forms of documents as well as other systems and agents. For example, if the user is looking for information on the possible side-effects of a particular weight-loss product, Agent A4 can extract the necessary data from the product monograph, request this information from the vendor’s or producer’s agents, or find people who used this product and are willing to share their experience. In the last case, A4 can supply the user with available contact information.
In the second scenario, the query comes from outside, and Agent A4 plays the role of a gate-keeper by filtering the incoming requests and limiting the amount of information to be supplied in response to a query, based on constraints set by the user (e.g., a list of friendly agents) and on world knowledge (e.g., information can be given to a reliable long-term partners but not to an unknown company).
CONCLUSION
In this chapter, we have described a model-based knowledge acquisition tool for user profiling for electronic commerce applications. The tool aims to reduce the burden on the user’s side while providing a sense of control and trust. The tool is based on a selected user model and is agent-mediated.
Based on the customer’s shopping behavior, the user’s personalization needs are identified, and an appropriate user model is described. The user model presented in this chapter consists of a directed acyclic graph of PIEs (Preference Indication by Example). This model is motivated by the perceived need to broaden the coverage of the domain of products while dealing with a virtual or electronic shopping mall.
Knowledge about the consumer is acquired using different techniques, ranging from fill-in forms and dialogue to the observation of user actions and machine learning. Analysis of user data is done by the processing Agent A1 and by the Web log mining module. The validation Agent A2 deals with conflict resolution and interacts with the user via dialogues.
Our tool is domain-dependent. If the domain is changed, the ontology and other related data have to be changed, but the overall structure of the system remains the same. The tool allows dynamic user profiling that goes through a constant monitoring, validation, and upgrade cycle. Our ongoing research is still focusing on this last issue.
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ENDNOTES
1 Preliminary work and first version of the system was described in Abi-Aad et al. (2001).
2 The term Preference Indication by Example was introduced in Abi-Aad (2001).
3 http://www.trl.ibm.com/projects/mrm/dp/index_e.htm
4 Information about various aspects of agents’ development and use can be found at http://agents.umbc.edu/
5 Other attributes often considered pertinent for agents include reactivity, temporal continuity, personality, and mobility (see Etzioni & Weld, 1995;
Franklin & Graesser, 1996).