Orchestrating context-aware systems

Cádiz, Alfredo; González, Sebastián; Mens, Kim

doi:10.1145/1595768.1595771

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Cadiz et al [10] or Rahnama et al [11] and their work enables us to work with more users and in various systems. Unifying context-aware systems into frameworks enables us to gather usage logs more globally.…”

Section: Related Workmentioning

confidence: 99%

Context Inference Using Correlation in Human Behaviour

Zelenik

Bieliková

2012

2012 Seventh International Workshop on Semantic and Social Media Adaptation and Personalization

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Abstract-Context-aware information retrieval received a significant amount of attention last years. This paper addresses some of the challenges in context acquisition. It is focused on a method for inference of unavailable contextual information using machine learning. The method for context inference is based on observed behaviour of individual user and virtual communities of similar users. We work with with contextual information such as location, time or weather in the domain of news recommending. We discuss the role of user behaviour and its significant impact on the actual information need, which directly influences information retrieval or recommendation process. In experiments we demonstrate the impact of inferred context on the recommendation process and its precision and recall. We use behaviour of news readers to predict their interest in news. We present context-aware recommendation which is supported by our method for context inference.

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Section: Related Workmentioning

confidence: 99%

Context Inference Using Correlation in Human Behaviour

Zelenik

Bieliková

2012

2012 Seventh International Workshop on Semantic and Social Media Adaptation and Personalization

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“…Research made by Cadiz et al [4] or Rahnama et al [15] enables us to work with more users and in various systems. By using standardized frameworks and unifying context-aware systems, we are able to gather usage logs.…”

Section: Related Workmentioning

confidence: 99%

Reducing the sparsity of contextual information for recommender systems

Zelenik

Bieliková

2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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Our work focuses on the improvement of the accuracy of context-aware recommender systems. Contextual information showed to be promising factor in recommender systems. However, pure context-based recommender systems can not outperform other approaches mainly due to high sparsity of contextual information. We propose an idea to improve accuracy of context based recommender systems by context inference. Context inference is based on effect discovered by analyses of the context as a factor influencing user needs. Analyses of the news readers reveals existence of behavioural correlation which is the main pillar of proposed context inference. Method for context inference is based on collaborative filtering and clustering of web usage (as a non-discretizing alternative to association rules mining).

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Context Similarity Evaluation: Inferring How Users Can Collectively Collaborate Together in a Pervasive Environment

McGovern

2013

2013 International Conference on Cloud and Green Computing

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Orchestrating context-aware systems

Cited by 5 publications

References 7 publications

Context Inference Using Correlation in Human Behaviour

Context Inference Using Correlation in Human Behaviour

Reducing the sparsity of contextual information for recommender systems

Context Similarity Evaluation: Inferring How Users Can Collectively Collaborate Together in a Pervasive Environment

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