Context-Aware HCI Service Selection

Abstract

Abstract. Context-awareness has become a key issue in Human-Computer Interaction(HCI) to provide better user experience under multi-device and multi-modal environment. With this intuition, we have proposed a web service based framework, which associates interactions with services, and provided service selection mechanism using context knowledge to achieve smart interaction migration [1]. A fundamental problem of such a service-oriented framework for interaction migration is to design an effective while scalable algorithm for service selection. In this paper, we propose a service selection algorithm considering not only context information and user preferences but also inter-service relations such as relative location. Our algorithm detects interaction hot spots within user active scope and presents the best service combination based on evaluation of interaction effectiveness. We also conduct simulation and the results illustrate that our algorithm is effective and scalable for interaction service selection.

Service selection in mobile environments: considering multiple users and context-awareness

“…service objects to conduct a multi-user context-aware process are usually confronted with an information overload problem (cf. Shen, Wang, Tang, Luo, & Guo, 2012; Zhang et al, 2009) since there often exist many alternative service objects for realising each action of such a process (referring to the example above, TripAdvisor lists over 3,000 different restaurants for having lunch in Munich 2 ). More precisely, when taking into account multiple users and context information, a service selection problem of high complexity results since it requires to consider dependencies that exist within a user’s service composition as well as among different users’ service compositions.…”

“…Few of them also consider context-based dependencies that could exist within a certain part or the entire service composition of a user (e.g. Deng et al, 2016; Shen et al, 2012; Xu & Jennings, 2010; Yu & Reiff-Marganiec, 2009; Zhou et al, 2008). However, those approaches focus solely on a single user and thus on context-based dependencies within a single user’s service composition.…”

“…Examples for such context information are daytime-dependent availability of service objects (i.e. business hours), price discount on a certain set of service objects, and the distance between different service providers or devices (Shen et al, 2012; Yu & Reiff-Marganiec, 2009; Zheng, Zhang, & Lyu, 2014; Zhou et al, 2008). Addressing this dynamic nature of context information leads to context-based dependencies between several or all service objects (Heinrich & Lewerenz, 2015; Zhou et al, 2008).…”

Every Cloud Has a Push Data Lining: Incorporating Cloud Services in a Context-Aware Application

“…The design and development of context-aware solutions [1][2][3][4] aim to align information and services with the needs of users as well as the characteristics of mobile devices and underlying networks. The significance of context-aware solutions can be realised through a wide range of applications in different areas such as personalization of services, marketing, advertisement, healthcare, traffic management, and dynamic configuration of computing devices and networks.…”

Advances in Network-Based Information Systems

“…In particular, autonomous selection of services requires a scalable cognitive algorithm for context or ambience awareness based on the user interactions. The next paper [4] "Context-aware HCI Service Selection" by Yao Shen, Minjie Wang, Xiaoxin Tang, Yi Luo, and MinyiGuo proposes a service-oriented framework for interaction migration and context-driven service selection in the multi-modal environment. The intelligent service-matching algorithm consists of three different processes for service properties, user preferences, and environmental contexts within devices and locally calculates the matching degree for service selection.…”