Multidimensional User Data Model for Web Personalization

Anil, Nithin K.; Kurian, Sharath Basil; Abahai, T Aby; Varghese, Surekha Mariam

doi:10.5120/11896-7955

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…This method automatically adapts the content for any profile and allows us to obtain the most generic classifier to each one. A multi-dimensional user data model and its application in web search (Anil et al, 2013). Online and offline activities of the user are tracked for creating the user model.…”

Section: User Profile Approachesmentioning

confidence: 99%

Web service selection based on QoS and user profile

2020

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The web services are from different sources, heterogeneous, and of large volume. The user is in a crucial situation to select the best web services. The web service selection process aims to discovery the desired web services; as it allows to select the best web services to users' query. In particular, various web services have the same functionalities, so we need another factor to select the desired web services, which is the quality of service (QoS). The QoS has an important role in the web service selection process, it aims to classify the web service that have same functionality. This paper focuses on different concepts of the QoS. We present a new approach that is composed by two services; its role is primarily the best web service selection in relation with users' query and profile. In our approach, a better knowledge of user behavior is important because users can participate in research design and construction. The experiment shows that our method can accurately recommend the needed web services in a faster time.

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Section: User Profile Approachesmentioning

confidence: 99%

Web service selection based on QoS and user profile

2020

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“…Some tradeoffs and limitations of the above traditional collaborative filtering approaches have been discussed in the literature [31,32]. To cope with the limitations, several systems use item-to-item collaborative filtering, which is a variation of collaborative filtering approach [32,38].…”

Section: Web and Cloud Services Personalizationmentioning

confidence: 99%

“…There are three common approaches for Web personalization [31], namely utilization of rule-based systems, content-based filtering, and collaborative filtering.…”

Section: Web and Cloud Services Personalizationmentioning

confidence: 99%

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On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

et al. 2018

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Cloud service providers typically compose their services from a number of elementary services, which are developed in-house or built by third-party providers. Personalization of composite services in mobile environments is an interesting and challenging issue to address, given the opportunity to factor-in diverse user preferences and the plethora of mobile devices at use in multiple contexts. This work proposes a framework to address personalization in mobile cloud-service provisioning. Service personalization and adaptation may be considered at different levels, including the user profile, the mobile device in use, the context of the user and the composition specification. The user's mobile device and external services are typical sources of context information, used in our proposed algorithm to elicit context-aware services. The selection process is guided by quality-of-context criteria that combine cloudservice provider requirements and user preferences. Hence, the paper proposes an integrated framework for enhancing personalized mobile cloud-services, based on a composition approach that adapts context information using a common model of service metadata specification.

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