Flexible Query Answering Systems 2015: Proceedings of the by Troels Andreasen, Henning Christiansen, Janusz Kacprzyk,

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By Troels Andreasen, Henning Christiansen, Janusz Kacprzyk, Henrik Larsen, Gabriella Pasi, Olivier Pivert, Guy De Tré, Maria Amparo Vila, Adnan Yazici, Slawomir Zadrożny

This quantity includes the papers offered on the 11th versatile question Answering structures 2015 (FQAS-2015) hung on October 26-28, 2015 in Cracow, Poland. The overseas meetings on versatile question Answering platforms (FQAS) are a chain of premiere meetings concentrating on the most important factor within the info society of delivering effortless, versatile, and intuitive entry to details and data to each person, even individuals with a truly constrained computing device literacy. In concentrating on this factor, the convention attracts on a number of learn parts, akin to info retrieval, database administration, details filtering, wisdom illustration, smooth computing, administration of multimedia info, and human-computer interplay. The convention presents a special chance for researchers, builders and practitioners to discover new rules and ways in a multidisciplinary discussion board.

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Additional resources for Flexible Query Answering Systems 2015: Proceedings of the 11th International Conference FQAS 2015, Cracow, Poland, October 26-28, 2015

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It can be observed that there was some small improvements after adjusting the model, which indicates that the model can be further improved if consumers’ search behaviors can be better understood. 26 M. Zhang et al. Fig. 1401 5 Conclusion Consumer information search has long been recognized as an important procedure before consumers making purchase decisions, which reflects their purchase intentions. In this paper, we have studied the search behaviors of mobile consumers. In doing so, the click times in terms of different timeslots and geographical areas have been analyzed, where the contextual information including time and location has great influence on consumer search behaviors.

Keywords User modeling · Preference learning · Decision support · Recommender systems · Generalized monotonicity constraint · Distribution of quality of learning along users 1 Introduction, Related Work, Data Examples Main motivation of this paper is the huge and always growing number of users as well as the growing amount of e-shops. Main task is easy, efficient, flexible, human-friendly and intuitive recommender systems personalized to each single user specifically. Our work can be considered as a work on a multi-user monotone prediction problems, in which the target variable is non-decreasing given an increase of the explanatory variables (for each user separately).

2 MUP Monotone User Preferences

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