9,386 research outputs found
QoE Modelling, Measurement and Prediction: A Review
In mobile computing systems, users can access network services anywhere and
anytime using mobile devices such as tablets and smart phones. These devices
connect to the Internet via network or telecommunications operators. Users
usually have some expectations about the services provided to them by different
operators. Users' expectations along with additional factors such as cognitive
and behavioural states, cost, and network quality of service (QoS) may
determine their quality of experience (QoE). If users are not satisfied with
their QoE, they may switch to different providers or may stop using a
particular application or service. Thus, QoE measurement and prediction
techniques may benefit users in availing personalized services from service
providers. On the other hand, it can help service providers to achieve lower
user-operator switchover. This paper presents a review of the state-the-art
research in the area of QoE modelling, measurement and prediction. In
particular, we investigate and discuss the strengths and shortcomings of
existing techniques. Finally, we present future research directions for
developing novel QoE measurement and prediction technique
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
Statistical relational learning with soft quantifiers
Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results
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