Context-Aware Profiles

Ferscha, Alois; Hechinger, M.; Riener, Andreas; Schmitzberger, Heinrich; Franz, Matthias; Rocha, Md.S.; Zeidler, Andreas

doi:10.1109/icas.2006.18

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…The PPDL (pervasive profile description language) is an XML-based language that can be used to describe preferences of peers within situations peers operate [17].…”

Section: Context Types and Categorization Schemesmentioning

confidence: 99%

MyRestaurant: A Smart Restaurant with a Recommendation System

al.¹

2019

IJCDS

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In this paper, we discuss, in an internet of things environment, our solution regarding context management in the area of food services. We propose a system that automates the different services offered in a restaurant and connects the various equipment of this restaurant, in order to simplify and facilitate the work of employees using this system. We also have integrated a content-based recommendation module, that is based on the use of a similarity measure to keep track of the client order history in order to suggest or recommend to him similar dishes for his next orders.

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“…The PPDL (pervasive profile description language) is an XML-based language that can be used to describe preferences of peers within situations peers operate [17].…”

Section: Context Types and Categorization Schemesmentioning

confidence: 99%

MyRestaurant: A Smart Restaurant with a Recommendation System

al.¹

2019

IJCDS

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“…For instance, Ferscha et al [8] employ hierarchically-organized profiles that are exchanged between mobile peers in spatial proximity. Such profiles contain peer preferences (time, geographical position, owner, environmental conditions, etc.)…”

Section: Profiling Information Behavior Contextsmentioning

confidence: 99%

Capturing User Contexts: Dynamic Profiling for Information Seeking Tasks

Shtykh

Jin

2008

2008 Third International Conference on Systems and Networks Communications

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Knowing each user's information needs is important for information systems to better facilitate human information activities. This is especially important in the days of information overload we are experiencing today. However, knowing and correctly applying individual information needs is extremely difficult, often impossible. Yet knowing multiple contexts of user information behavior can give us some conception (or a hint) of conceivable information a user tries to obtain in a particular context. In this paper we focus on capturing such information contexts into dynamically changing profiles which are further used to facilitate the user's information seeking activities.

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“…For instance, Carrillo-Ramos et al [48], in attempt to adapt information to a nomadic user by taking context of use into consideration, introduce Contextual User Profile which consists of user preferences and current context (location, mobile device features, access rights, user activities) of use. Ferscha et al [58] propose context-aware profile description language (PPDL) expressing mobile peers' preferences with respect to a particular situation. Finally, some attempts to provide more holistic approaches to profile structuring, such as Gargi [59]'s Information Navigation Profile (INP) defining attributes for characterizing IR interfaces, interaction and presentation modes, are made resulting in complex profiles that consist of multiple search criteria.…”

Section: Structural Componentsmentioning

confidence: 99%

“…For instance, while context of a user is being learnt, for instance, from documents and ontologies [60], multiple context attributes like environmental and other properties (time, location, temperature, space, speed, etc.) are considered in [61] to define context-aware profiles. And probably because of such differences related to application domains, there is very little exchange of verified practices among researchers working on personalization in different areas and, despite available similarities in various domains, the one-sided views on context are not rare.…”

Section: On User Contextsmentioning

confidence: 99%

A human-centric integrated approach to web information search and sharing

Shtykh

Jin

2011

Hum. Cent. Comput. Inf. Sci.

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In this paper we argue a user has to be in the center of information seeking task, as in any other task where the user is involved. In addition, an essential part of user-centrism is considering a user not only in his/her individual scope, but expanding it to the user's community participation quintessence. Through our research we make an endeavor to develop a holistic approach from how to harnesses relevance feedback from users in order to estimate their interests, construct user profiles reflecting those interests to applying them for information acquisition in online collaborative information seeking context. Here we discuss a human-centric integrated approach for Web information search and sharing incorporating the important user-centric elements, namely a user's individual context and 'social' factor realized with collaborative contributions and co-evaluations, into Web information search.

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Context-Aware Profiles

Cited by 11 publications

References 5 publications

MyRestaurant: A Smart Restaurant with a Recommendation System

MyRestaurant: A Smart Restaurant with a Recommendation System

Capturing User Contexts: Dynamic Profiling for Information Seeking Tasks

A human-centric integrated approach to web information search and sharing

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