A hybrid model for business process event and outcome prediction

Large service companies run complex customer service processes to provide communication services to their customers. The flawless execution of these processes is essential because customer service is an important differentiator. They must also be able to predict if processes will complete successfully or run into exceptions in order to intervene at the right time, preempt problems and maintain customer service. Business process data are sequential in nature and can be very diverse. Thus, there is a need for an efficient sequential forecasting methodology that can cope with this diversity. This paper proposes two approaches, a sequential k nearest neighbour and an extension of Markov models both with an added component based on sequence alignment. The proposed approaches exploit temporal categorical features of the data to predict the process next steps using higher order Markov models and the process outcomes using sequence alignment technique. The diversity aspect of the data is also added by considering subsets of similar process sequences based on k nearest neighbours. We have shown, via a set of experiments, that our sequential k nearest neighbour offers better results when compared with the original ones; our extension Markov model outperforms random guess, Markov models and hidden Markov models

Evaluation of recommender systems in streaming environments

Evaluation of recommender systems is typically done with finite datasets. This means that conventional evaluation methodologies are only applicable in offline experiments, where data and models are stationary. However, in real world systems, user feedback is continuously generated, at unpredictable rates. Given this setting, one important issue is how to evaluate algorithms in such a streaming data environment. In this paper we propose a prequential evaluation protocol for recommender systems, suitable for streaming data environments, but also applicable in stationary settings. Using this protocol we are able to monitor the evolution of algorithms' accuracy over time. Furthermore, we are able to perform reliable comparative assessments of algorithms by computing significance tests over a sliding window. We argue that besides being suitable for streaming data, prequential evaluation allows the detection of phenomena that would otherwise remain unnoticed in the evaluation of both offline and online recommender systems.Comment: Workshop on 'Recommender Systems Evaluation: Dimensions and Design' (REDD 2014), held in conjunction with RecSys 2014. October 10, 2014, Silicon Valley, United State

Technique for improving care integration models

Recent developments in technologies and improved life style have had a positive impact on prolonging human life contributing to the increasing elderly population. As a consequence, many countries (particularly developed ones) started to experience higher proportions of elderly people (over 65). This has consequently generated the need for care for the elderly that is necessitating the integration of health and social care to accommodate their complex needs. A number of modelling methods have been employed to assist those concerned to cope with health and social care but albeit separately. The literatures so far, identified several techniques that have been employed mostly to model the care integration. However, literatures also suggest that there are some challenges still persist when modelling integrated care. It can be argued that these techniques are not capable of handling the complexities associated with the requirements of integrated systems. This paper attempts to prove the reason why despite the fact that many models of integrated care have been developed, problems are still exist. Based on the literatures, the problems exist due to the unsuitable techniques used to model the IC systems as most of the developed models are using single technique. Therefore, new technique to improve the care integration model is suggested

NewsMe: A case study for adaptive news systems with open user model

Adaptive news systems have become important in recent years. A lot of work has been put into developing these adaptation processes. We describe here an adaptive news system application, which uses an open user model and allow users to manipulate their interest profiles. We also present a study of the system. Our results showed that user profile manipulation should be used with caution. © 2007 IEEE

A hybrid information approach to predict corporate credit risk

This article proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced-form models to produce a model combination based credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced-form models contribute more in the pooled strategies for speculative grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names