Expertise recommendation in online communities

Online communities recently have made an alternative way for professionals to share expertise. This increasing usage of online communities enabled us to find experts via user generated contents and user activities. Traditionally locating experts in such websites consumes a large amount of time and requires vast human processes, while recent boosting in artificial intelligence and data mining can be a game changer. Hence, in this dissertation, we propose a set of algorithms and techniques, to find and recommend experts in online communities, especially online community question answering (CQA) websites. We systematically reviewed existing research and techniques for the expert recommendation in CQA with comparisons concerning their advantages and shortcomings. One issue found in CQA websites is low participation in posts. This limits the effectiveness of CQA based knowledge sharing, as well as at large diminishes performances of expert recommendation algorithms. Thus, we took Stack Overflow as the subject of study, which is a successful programming CQA website. We propose to recommend experts in the websites, to help lessen untouched questions, and ultimately enrich the contents in the community. Neural networks based techniques are proposed to produce representations for user generated contents and topics, then based on vector similarities we rank the posts by topics. Finally the ranked posts are used to refer the expert content creators, who can be promising in resolving new problems. Alternatively, it can be argued that scarcely a research focuses on multi-domain recommendation in CQA, while experts with more than one specialisations are often required to solve complicated, multi-discipline problems. Extended from our aforementioned work, we looked into StackExchange Networks, the parent website of Stack Overflow, and multiple knowledge domains are taken into consideration. Since more facets of experts are involved, tensor can be the desirable receptacle of data, and its decomposition is instinctively the technique for expert recommendation. Furthermore, due to the hierarchical structure of data source, a relationship tree is modelled to guide the decomposition, and it is proven effective in alleviating sparseness issue, which helps our decomposition. Discussions on open issues and future research directions are also included in this dissertation