[Objective] This paper reviews the expert retrieval and expert ranking literature to provide theoretical foundations for future studies. [Coverage] 65 papers were retrieved from the Web of Science (WOS), CNKI and other databases using the keywords of “expert retrieval”, “expert ranking”, and “ranking fusion”. [Methods] We analyzed research evaluating expert retrieval and fusion rankings, aiming to solve the issues of insufficiency of expert coverage and heavy computation of expert features. [Results] We found that most expert retrieval system adopted the relationship attribute fusion method, and the credibility of search results was decided by the users’ satisfaction and quality of the retrieved documents. Expert ranking was established by FRM, PageRank, D-S theory, social network and complex network analysis. Empirical research showed that the fusion ranking results were generally better than the baseline ones. [Limitations] More comparison of research among different ranking methods was needed. [Conclusions] Related studies help us building expert consulting platform from the perspective of expert information organization, expert selection and expert opinion fusion.
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