Expertise recommendation in online communities

Online communities recently have made an alternative way for professionals to shareexpertise. This increasing usage of online communities enabled us to find experts viauser generated contents and user activities. Traditionally locating experts in suchwebsites consumes a large amount of time and requires vast human processes, whilerecent 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 findand recommend experts in online communities, especially online community questionanswering (CQA) websites. We systematically reviewed existing research andtechniques for the expert recommendation in CQA with comparisons concerningtheir advantages and shortcomings.One issue found in CQA websites is low participation in posts. This limits theeffectiveness of CQA based knowledge sharing, as well as at large diminishes performancesof expert recommendation algorithms. Thus, we took Stack Overflow asthe subject of study, which is a successful programming CQA website. We proposeto recommend experts in the websites, to help lessen untouched questions, and ultimatelyenrich the contents in the community. Neural networks based techniques areproposed to produce representations for user generated contents and topics, thenbased on vector similarities we rank the posts by topics. Finally the ranked postsare used to refer the expert content creators, who can be promising in resolving newproblems.Alternatively, it can be argued that scarcely a research focuses on multi-domainrecommendation in CQA, while experts with more than one specialisations are oftenrequired to solve complicated, multi-discipline problems. Extended from ouraforementioned work, we looked into StackExchange Networks, the parent websiteof Stack Overflow, and multiple knowledge domains are taken into consideration.Since more facets of experts are involved, tensor can be the desirable receptacle ofdata, and its decomposition is instinctively the technique for expert recommendation.Furthermore, due to the hierarchical structure of data source, a relationshiptree is modelled to guide the decomposition, and it is proven effective in alleviatingsparseness issue, which helps our decomposition.Discussions on open issues and future research directions are also included in thisdissertation