Ontology-based user profile learning

Eyharabide, Victoria; Amandi, Analía

doi:10.1007/s10489-011-0301-4

Cited by 42 publications

(18 citation statements)

References 21 publications

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“…In (Zanker and Jessenitschnig, 2009), the authors present a simple attribute-value pair dictionary to model the user through the explicit elicitation of user requirements. A richer user model is presented in (Eyharabide and Amandi, 2012), where the authors used a machine learning process to capture the user profile and context into a domain ontology.…”

Section: User Modelingmentioning

confidence: 99%

“…Our work tries to balance between simple (Zanker and Jessenitschnig, 2009) and complex models (Eyharabide and Amandi, 2012) with the goal of having an efficient but still rich user model. Other works, like Cantador et al andMoahedian et al (Cantador et al, 2008, Movahedian andKhayyambashi, 2014), are similar to our proposed user model, since we use tags and keywords to build a lax ontology.…”

Section: User Modelingmentioning

confidence: 99%

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Social Metrics Applied to Smart Tourism

Cervantes

Gutiérrez

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general) to discover cultural heritage, points of interest and available services in a smart city. This data can be used to personalize recommendations in a smart tourism application. Our approach is based on flow centrality metrics typically used in social network analysis: flow betweenness, flow closeness and eccentricity. These metrics are useful to discover relevant nodes within the network yielding nodes that can be interpreted as suggestions (venues or services) to users. We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations. We also present challenges and results from a prototypical implementation applied to the case study of the City of Puebla, Mexico.

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Section: User Modelingmentioning

confidence: 99%

Section: User Modelingmentioning

confidence: 99%

Social Metrics Applied to Smart Tourism

Cervantes

Gutiérrez

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Human dialog knowledge is designed into two layers: domain and discourse knowledge, and it is integrated with the data-driven model in generation time. Recent studies have also addressed important points related to the use of semantic agents, multilevel concepts and behavioral model [47], automatic user-profil generation [50], or using physiological signals to detect natural interactive behaviors [89,100]. A statistical user model supported by a RTree structure and several search spaces is presented in [31].…”

Section: Modeling the User Intentionmentioning

confidence: 99%

Modeling the user state for context-aware spoken interaction in ambient assisted living

2014

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Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfil these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influenc of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.

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“…Endowing the system with inter-ontology capabilities [42] or semantic negotiation [22] are some solutions to this problem. Besides, Ontology support is a useful way of enhancing the model capabilities [23], unifying different terms for the same concept and enriching the available information regarding the current user.…”

Section: State Of the Art In User Modelingmentioning

confidence: 99%

Statistical user model supported by R-Tree structure

et al. 2013

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This paper is about developing a group user model able to predict unknown features (attributes, preferences, or behaviors) of any interlocutor. Specifically, for systems where there are features that cannot be modeled by a domain expert within the human computer interaction. In such cases, statistical models are applied instead of stereotype user models. The time consumption of these models is high, and when a requisite of bounded response time is added most common solution involves summarizing knowledge. Summarization involves deleting knowledge from the knowledge base and probably losing accuracy in the medium-term. This proposal provides all the advantages of statistical user models and avoids knowledge loss by using an R-Tree structure and various search spaces (universes of users) of diverse granularity for solving inferences with enhanced success rates. Along with the formalization and evaluation of the approach, main advantages will be discussed, and a perspective for its future evolution is provided. In addition, this paper provides a framework to evaluate statistical user models and to enable performance comparison among different statistical user models.

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Ontology-based user profile learning

Cited by 42 publications

References 21 publications

Social Metrics Applied to Smart Tourism

Social Metrics Applied to Smart Tourism

Modeling the user state for context-aware spoken interaction in ambient assisted living

Statistical user model supported by R-Tree structure

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