Expertise-seeking research studies how people search for expertise and choose whom to contact in the context of a specific task. An important outcome are models that identify factors that influence expert finding. Expertise retrieval addresses the same problem, expert finding, but from a system-centered perspective. The main focus has been on developing content-based algorithms similar to document search. These algorithms identify matching experts primarily on the basis of the textual content of documents with which experts are associated. Other factors, such as the ones identified by expertise-seeking models, are rarely taken into account. In this article, we extend content-based expert-finding approaches with contextual factors that have been found to influence human expert finding. We focus on a task of science

Abstract—In this work, we address the problem of distributed expert finding using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the performance of distributed search, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct exten-sive experiments on several datasets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90 % in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20%. The overall cost of finding an expert by our truthful mechanism is about 20 % of the untruthful methods, e.g.. the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.

Abstract not found

further improved by training, expert search performance is also generally enhanced. of inf docu an e be ac rn ex e for evidence available on the Web for searching for company experts. A natural source for looking for experts on the Web is to query existing Web search engines (WSEs) for evidence support-ing the expertise of a set of candidates on a particular topic of interest. Unfortunately, however, WSEs are not tailored expert search systems, and hence we cannot measure their performance at an expert search task directly. An alternative would be to use the WSE result listing as the first layer in an expert search system. However, this is difficult because we cannot make

Abstract — Diverse environments have studied expert search viz. educational communities, enterprises etc. The system refers to a universal expert search problem: Most important is expert search on the internet, that considering ordinary WebPages and people names. But it is having primarily two challenging issues: Unreliable quality of WebPages and WebPages containing full of unwanted information; usually indistinct and confusing expertise evidence are spread in web pages. To address the task of finding experts on the web, numerous solutions have been proposed. Relevance is the main concern in usual organizational expert search, so it is essential to take advantage of the huge amount of co-occurrence information to evaluate relevance and reputation of an individual name for a query theme. This paper mainly proposes a multithreaded ranking algorithm which considers people names and ordinary web pages. We are complementing both document and proximity-based approaches to expert finding by importing global evidence of expertise. Our proposed system also deals with the problem of extraction and disambiguation of person name. An NLP technique to adjust association scores among people and words is also applied by proposed system.

Expert search has been studied in various contexts. We inspect a commonexpert search problem. Piercing experts on the web, where thousands of names and millions of webpages are considered. It has two challenging issues: 1) web pages could be of full of noises and low quality2) The expertise evidences scattered in web pages are usually arises confusion. We propose to leverage the large amount of concomitant information to assess relevance and reputation of a person name for a query topic. The concomitant structure is modeled using a hyper graph, on which a heat disseminates based ranking algorithm is proposed. Query keywords are regarded as sources of heat and a name of person which has strong link with the query (i.e., frequently