Shearin, S. and Liebermann, H. Intelligent profiling by example. In Proceedings of the ACM Conference on Intelligent User Interfaces (Santa Fe, NM, Jan.). 2001, 145-151.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....and structures of the ontology, as well as on the features of the infrastructure that exploit such descriptions. How to effectively obtain descriptions of user tasks is addressed by the fields of Artificial Intelligence and Human Computer Interaction, and is not central to the proposed research [3,38,42,47]. I will examine and incorporate results from three areas of research. First, although Architectural Description Languages (ADLs) have been traditionally used to describe the interconnections of software components [17] I will explore the feasibility of using ADLs to describe the ....

S. Shearin, H. Lieberman. Intelligent Profiling by Example. Proc. International Conference on Intelligent User Interfaces (IUI 2001). Sante Fe, New Mexico, January 2001.

....the subject of increased interest in the research community ( 6, 7] We consider compensating for this shortcoming by letting the user pick the most preferred solution from a larger displayed set D of k solutions. Such an approach has been taken in numerous practical systems, for example in [8, 9, 10, 11, 13]. The process will be sound, i.e. allow the user to find the target solution, only if the displayed set actually contains the solution. It turns out that this heavily depends on the model used for selecting displayed solutions as well as on the number of preferences that have been stated. In this ....

S. Shearin and H. Lieberman. Intelligent Profiling by Example. Proceedings of the International Conference on Intelligent User Interfaces (IUI 2001.

....information services in a dynamic and distributed environment. Data come from multiple sources, they are heterogeneous (not every product has the same features) and products are configurable rather than simple. For example, in the travel industry, a portal (such as Expedia [6] or Travelocity [21]) is much more likely to offer products of a variety of different companies and integrated products (e.g. packages including air, car rental, and event related parts) for coordinated planning tasks. While there is often a database of constituent parts (such as segments of flights in flight ....

Shearin, S., and Lieberman. Intelligent Profiling by Example. In Proceedings of the International Conference on Intelligent User Interfaces (IUI

....initially. 2) criteria are highly variable. For example, the same user may be planning a business trip in the morning, where time constraints are the most important, and a trip for his family in the evening, with price as the overriding criterion. We have adopted a solution, similar to [17], based on the principle that people find it easy to critique proposed solutions if there is nothing to critique, they have already found what they were looking for. We implement this using a mixed initiative dialog: the system generates 30 solutions that are good according to the known ....

....time, or both. RELATED WORK Information seeking systems The query based information retrieval paradigm no longer suffices for the information seeking task in multivariate spaces. Modern tools emphasize assisting users to formulate their goals [3,4] helping them solicit hidden criteria [17, 19], comparing a large collection of items in context, and providing information about the structure of the information space. A differentiating factor of our work is that we have severe constraints on users interaction with data sources, and the heterogeneous and configurable nature of data ....

Shearin, S. and Lieberman, H. Intelligent Profiling by Example, in Proceedings of Conference on Intelligent User Interfaces, ACM Press, 2001.

....initially. 2) criteria are highly variable. For example, the same user may be planning a business trip in the morning, where time constraints are the most important, and a trip for his family in the evening, with price as the overriding criterion. We have adopted a solution, similar to [5], based on the principle that people find it easy to critique proposed solutions if there is nothing to critique, they have already found what they were looking for. We implement this using a mixed initiative dialog: the system generates 30 solutions that are good according to the known ....

Shearin, S. and Lieberman, H. Intelligent Profiling by Example, in Proceedings of Conference on Intelligent User Interfaces, ACM Press, 2001.

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Shearin, S. and Liebermann, H. Intelligent profiling by example. In Proceedings of the ACM Conference on Intelligent User Interfaces (Santa Fe, NM, Jan.). 2001, 145-151.

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S. Shearin and H. Lieberman. Intelligent profiling by example. In IUI '01, 2001.

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Shearin, S., and Lieberman, H. Intelligent Profiling by Example. Proceedings of the 6th international Conference on Intelligent User Interfaces (Santa Fe, New Mexico, USA, 2001.

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Sybil Shearin and Henry Lieberman. Intelligent Profiling by Example . In Proceedings of the Conference of Intelligent User Interfaces (IUI'01). ACM Press, 2001.

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Shearin, S. and Lieberman, H. Intelligent Profiling by Example. in Proceedings of the Conference on Intelligent User Interfaces, ACM Press, 2001.

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S. Shearin, H. Lieberman. Intelligent Profiling by Example. Proc. International Conference on Intelligent User Interfaces (IUI 2001). Sante Fe, New Mexico, January 2001.

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Shearin, S. and Lieberman, H. Intelligent Profiling by Example. in Proceedings of the Conference on Intelligent User Interfaces, ACM Press, 2001.

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Shearin, S. and Lieberman, H. (2001). Intelligent profiling by example. In International Conference on Intelligent User Interfaces (IUI).

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