Context-Aware Recommendations on Mobile Services: The m:Ciudad Approach

Emrich, Andreas; Chapko, Alexandra; Werth, Dirk

doi:10.1007/978-3-642-04471-7_9

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…提供准确的个性化推荐,成为移动上下文研究的热点 [12,13,26,36−42] .移动用户的地理位置信息可以通过 GPS 定位、移动基站定位或者其他技术手段来获取或者推理得到.移动推荐系统领域的位置上下文信息可以从硬指标 (hard criteria)和软指标(soft criteria)角度来划分 [39,43] .硬指标是指必须要满足的条件,用来过滤项目;而软指标用来对项目集进行排序或预测用户对每个项目的偏好程度.位置上下文作为硬指标时,只有在满足当前位置上下文要求的项目才会被推荐;而作为软指标,是根据与当前位置上下文的匹配程度对项目进行排序.从功能上看, 将上下文分为硬指标和软指标,跟上下文与推荐系统的结合方式是一致的.…”

Section: 基于位置的移动推荐移动用户在不同的地理位置环境下偏好会有所不同如何利用位置上下文更准确地获取移动用户偏好并unclassified

Mobile Recommender Systems and Their Applications

Meng¹,

Hu²,

Wang³

et al. 2014

Journal of Software

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Mobile recommender systems have recently become one of the hottest topics in the domain of recommender systems. The main task of mobile recommender systems is to improve the performance and accuracy along with user satisfaction utilizing mobile context, mobile social network and other information. This paper presents an overview of the field of mobile recommender systems including key techniques, evaluation and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.

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Section: 基于位置的移动推荐移动用户在不同的地理位置环境下偏好会有所不同如何利用位置上下文更准确地获取移动用户偏好并unclassified

Mobile Recommender Systems and Their Applications

Meng¹,

Hu²,

Wang³

et al. 2014

Journal of Software

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“…In addition, semantic reasoners allow for the reasoning about the different context models and user preferences described by these ontology languages. For example, the European FP7 research project m:Ciudad demonstrates [9] an approach to integrate contextual information with other search attributes to enable efficient service retrieval and recommendation in mobile user and application scenarios. Ontologies are used to describe services, users, situations and their domain knowledge.…”

Section: Related Workmentioning

confidence: 99%

Mobile application usage prediction through context-based learning

Leroux

Roobroeck

Dhoedt

et al. 2013

Journal of Ambient Intelligence and Smart Environments

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The purchase and download of new applications on all types of smartphones and tablet computers has become increasingly popular. On each mobile device, many applications are installed, often resulting in crowded icon-based interfaces. In this paper, we present a framework for the prediction of a user's future mobile application usage behavior. On the mobile device, the framework continuously monitors the user's previous use of applications together with several context parameters such as speed and location. Based on the retrieved information, the framework automatically deduces application usage patterns. These patterns define a correlation between a used application and the monitored context information or between different applications. Furthermore, by combining several context parameters, context profiles are automatically generated. These profiles typically match with real life situations such as 'at home' or 'on the train' and are used to delimit the number of possible patterns, increasing both the positive prediction rate and the scalability of the system. A concept demonstrator for Android OS was developed and the implemented algorithms were evaluated in a detailed simulation setup. It is shown that the developed algorithms perform very well with a true positive rate of up to 90% for the considered evaluation scenarios.

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Designing Context Models for CARS Incorporating Partially Observable Context

Mettouris

Papadopoulos

2015

Modeling and Using Context

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Context-Aware Recommendations on Mobile Services: The m:Ciudad Approach

Cited by 6 publications

References 4 publications

Mobile Recommender Systems and Their Applications

Mobile Recommender Systems and Their Applications

Mobile application usage prediction through context-based learning

Designing Context Models for CARS Incorporating Partially Observable Context

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