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dc.contributor.authorKrishnan, G.S.-
dc.contributor.authorSowmya, Kamath S.-
dc.date.accessioned2020-03-30T10:02:46Z-
dc.date.available2020-03-30T10:02:46Z-
dc.date.issued2017-
dc.identifier.citation8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017, 2017, Vol., , pp.-en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/7772-
dc.description.abstractIn modern Web applications, the process of user-profiling provides a way to capture user-specific information, which then serves as a source for designing personalized user experiences. Currently, such information about a particular user is available from multiple online sources/services, like social media applications, professional/social networking sites, location based service providers or even from simple Web-pages. The nature of this data being truly heterogeneous, high in volume and also highly dynamic over time, the problem of collecting these data artifacts from disparate sources, to enable complete user-profiling can be challenging. In this paper, we present an approach to dynamically build a structured user profile, that emphasizes the temporal nature to capture dynamic user behavior. The user profile is compiled from multiple, heterogeneous data sources which capture dynamic user actions over time, to capture changing preferences accurately. Natural language processing techniques, machine learning and concepts of the semantic Web were used for capturing relevant user data and implement the proposed '3D User Profile'. Our technique also supports the representation of the generated user profiles as structured data so that other personalized recommendation systems and Semantic Web/Linked Open Data applications can consume them for providing intelligent, personalized services. � 2017 IEEE.en_US
dc.titleDynamic and temporal user profiling for personalized recommenders using heterogeneous data sourcesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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