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User modeling

About: User modeling is a research topic. Over the lifetime, 10701 publications have been published within this topic receiving 278012 citations.


Papers
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01 Jan 2007
TL;DR: Through an experiment in the domain of consumer laptop computers, it is shown that for parameter-based systems, outcomes, including measures for comfort and fit, increase in the expertise of the user, and that for novices, the needs-based interface results in better outcomes than the parameter- based interface.
Abstract: User design offers tantalizing potential benefits to manufacturers and consumers, including a closer match of products to user preferences, which should result in a higher willingness to pay for goods and services. There are two fundamental approaches that can be taken to user design: parameterbased systems and needs-based systems. With parameter-based systems, users directly specify the values of design parameters of the product. With needs-based systems, users specify the relative importance of their needs, and an optimization algorithm recommends the combination of design parameters that is likely to maximize user utility. Through an experiment in the domain of consumer laptop computers, we show that for parameter-based systems, outcomes, including measures for comfort and fit, increase in the expertise of the user. We also show that for novices, the needs-based interface results in better outcomes than the parameter-based interface.

183 citations

Patent
11 Apr 1997
TL;DR: In this article, a computer network is designed to use otherwise idle bandwidth of the network transmission medium to transfer targeted commercial and non-commercial information to users while minimizing the delay of normal network traffic.
Abstract: A computer network connects information providers and end-users of network services, facilitates direct information to users, and gathers user responses. The computer network is designed to use otherwise idle bandwidth of the network transmission medium to transfer targeted commercial and non-commercial information to users while minimizing the delay of normal network traffic. User reports containing demographics and user responses are generated ensuring user privacy. Information providers can access user reports without violating user anonymity.

182 citations

Patent
30 Mar 2012
TL;DR: In this paper, the authors describe a see-through, near-eye, mixed reality display device for providing customized experiences for a user, including an exercise program that is always with the user, provides motivation for the user and visually tells the user how to exercise, and lets the user exercise with other people who are not present.
Abstract: The technology described herein includes a see-through, near-eye, mixed reality display device for providing customized experiences for a user. The personal A/V apparatus serves as an exercise program that is always with the user, provides motivation for the user, visually tells the user how to exercise, and lets the user exercise with other people who are not present.

182 citations

Journal ArticleDOI
01 Feb 2012
TL;DR: The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests.
Abstract: Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors In these systems, the user behaviors are influenced by the hidden interests of the users Learning to leverage the information about user interests is often critical for making better recommendations However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand The goal is to build an item-oriented model-based collaborative-filtering framework The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin

181 citations

Patent
22 Dec 2005
TL;DR: In this paper, a method for managing interactive dialog between a machine and a user is claimed, which is based on determining at least one likelihood value which is dependent upon a possible speech onset of the user.
Abstract: A method for managing interactive dialog between a machine and a user is claimed. In one embodiment, an interaction between the machine and the user is managed by determining at least one likelihood value which is dependent upon a possible speech onset of the user. In another embodiment, the likelihood value can be dependent a model of a desire of the user for specific items, a model of an attention of the user to specific items, or a model of turn-taking cues. Further, the likelihood value can be utilized in a voice activity system.

180 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202327
202269
2021150
2020167
2019194
2018216