Understanding Context Before Using It

Bazire, Mary; Brézillon, Patrick

doi:10.1007/11508373_3

Cited by 274 publications

(170 citation statements)

References 16 publications

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“…With these dimensions as a guide, [56] identified 74 Facebook variables as potential predictors of tie strength. They presented a diagram showing percentages that indicate the predictive power of their top seven tie strength dimensions 9 and also the top three predictive variables for each dimension. From the 7 Note that this trust factor measures the tie strength in people's relationships.…”

Section: Social Knowledge Elicitation Modulesmentioning

confidence: 99%

“…This change in the trust computation is due to the computed relevance of those factors (the weight in the trust computation of some of them was almost insignificant in [11]) and the extraction power that Facebook gives us (status was impossible to automatically extract reliably). 9 Note that [56]'s top four dimensions are the ones that [58] used as definition of tie strength and therefore the ones that we have adopted, predictive variables that [56] provided, we selected the ones that were more representative of each major dimension and which could also be extracted from users' Facebook profiles (as we have said before, we are limited to the elicitation power that Facebook grants us). The percentages that [56] presented for [58]'s four dimensions were:…”

Section: Social Knowledge Elicitation Modulesmentioning

confidence: 99%

“…It includes the culture that the individual was educated or lives in, and the people and institutions with whom they interact. Besides, circumstantial life events, influences, and surroundings can further change our social abilities [9]. In our research, we study how to model these social-cognitive processes in human decision-making processes [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

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Section: Social Knowledge Elicitation Modulesmentioning

confidence: 99%

Section: Social Knowledge Elicitation Modulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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“…Bazire and Brézillon [1] collected 150 definitions from various areas of research. A widely adopted definition in the domain of computer science by Dey and Abowd [2] is:…”

Section: Service-oriented Contextmentioning

confidence: 99%

“…Trying to find the most stable service, the client might look up the nearest service by applying location information in the form of network hops or GPS coordinates. 1 A composite service utilizes time as context information to predict load peaks on employed services. Simple services anticipate load-distribution by observing the number of similar services available.…”

Section: Empowerment Through Contextmentioning

confidence: 99%

Sharing hierarchical context for mobile web services

Dorn

Dustdar

2006

Distrib Parallel Databases

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Context has the potential to enhance Web services in mobile environments to a great extent. Yet, challenges such as bandwidth restriction or dynamic changes require considerations that need to be reflected in the distribution of context. In this paper, we present a novel technique to share and control access to context. In structuring context information according to levels of granularity, we achieve context propagation at the relevant levels of detail while protecting the privacy of the user. Introducing the Context Access control, Subscription and Query Language (CASQL), we enable fine-grained subscriptions and control over context in our proposed Context Sharing Architecture (CoSAr). A collaborative work scenario accompanies our approach for a unified way of accessing context information.

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Pragmatic Research on Context Modeling and Use

Brézillon¹,

Turner²

2022

Modeling and Use of Context in Action

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Pragmatic approaches to solving problems in artificial intelligence arise due to fast technological evolution as well as the complexity of real-world tasks. These operational, bottom-up approaches are based on representing how to behave in specific contexts. In this chapter, pragmatic approaches are discussed from the perspective of agents (human and AI) using data, information, and knowledge in context. Information is what is interpreted by an agent from data about the world, and what is exchanged with other agents. Information is highly context-dependent and under the control of the knowledge that is in the agent's "mind". Context plays a pivotal role in the development of information and knowledge. Setting data, information and knowledge in context allows new approaches to modeling (human and AI) reasoning. Based on its contextual knowledge (the part of its knowledge concerning the current focus), a reasoner interprets and uses information and produces new information and knowledge. Concretely, the pragmatic approach is shown in three examples of context-based research addressing applications in the real world. Operational knowledge in these approaches leads to contextualization of reasoning and opens the door to context-based AI systems.

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Understanding Context Before Using It

Cited by 274 publications

References 16 publications

Development of a group recommender application in a Social Network

Development of a group recommender application in a Social Network

Sharing hierarchical context for mobile web services

Pragmatic Research on Context Modeling and Use

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