It is hoped by the author of this thesis that the proposed expertise inference approaches, that support the modelling of the user expertise information in SNSs, will be of benefit to both the research community and commercial applications. The proposed LSR-FGM will be useful for researchers who wish to model the language information of SNS users and those who wish to utilise the user’s social profiles for other inference tasks. The proposed SeTRL and DnTCom for topical expertise inference on Twitter will be useful for researchers who wish to model online users’ topical expertise by exploiting various social activities. Researchers could continue to contribute to new/advanced inference approaches in the future, with a target of the exploitation of various other social data (e.g. the involvement of multimedia data) or the inference of different aspects of user expertise (e.g. expertise related to the targeted tasks/projects).
It is also hoped that the proposed inference approaches will be of benefit to the providers of SNSs and those Web applications that linked to their users’ SNS accounts through the adoption of these proposed approaches. The case study experiments in this thesis have shown the benefits that these approaches can bring to a CQA service. As a result, more Web applications or SNS providers themselves may employ these approaches to gain a better understanding of their users, which would allow them to enhance their current services or even assemble new functionalities.
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