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Enhanced models for expertise retrieval using community-aware strategies
- IEEE Trans. SMC-B
, 2012
"... Abstract—Expertise retrieval, whose task is to suggest people with relevant expertise on the topic of interest, has received increasing interest in recent years. One of the issues is that previous algorithms mainly consider the documents associated with the experts while ignoring the community infor ..."
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Abstract—Expertise retrieval, whose task is to suggest people with relevant expertise on the topic of interest, has received increasing interest in recent years. One of the issues is that previous algorithms mainly consider the documents associated with the experts while ignoring the community information that is affiliated with the documents and the experts. Motivated by the observation that communities could provide valuable insight and distinctive information, we investigate and develop two community-aware strategies to enhance expertise retrieval. We first propose a new smoothing method using the community context for statistical language modeling, which is employed to identify the most relevant documents so as to boost the performance of expertise retrieval in the document-based model. Furthermore, we propose a querysensitive AuthorRank to model the authors ’ authorities based on the community coauthorship networks and develop an adaptive ranking refinement method to enhance expertise retrieval. Experimental results demonstrate the effectiveness and robustness of both community-aware strategies. Moreover, the improvements made in the enhanced models are significant and consistent. Index Terms—Community-aware strategy, expertise retrieval, language model, query-sensitive AuthorRank. I.
Recommendation on the Web Search by Using Co-Occurrence
"... ABSTRACT: In our day to day, the usage of internet and searching the information should be increases rapidly. Because of this, now a days we have facing the problems like whether the retrieving information would be noise free or not and having many confusions with the usage of keywords to get the e ..."
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ABSTRACT: In our day to day, the usage of internet and searching the information should be increases rapidly. Because of this, now a days we have facing the problems like whether the retrieving information would be noise free or not and having many confusions with the usage of keywords to get the exact result. To avoid this problem we are going to propose the concepts called Co-Occurrence and recommendation. These two concepts increases the effectiveness and of the result. By using the recommendation concept we have multiple choices to select the desired thing. The web search increases dramatically [1] user search performance leads to large number of confusions. We examine a general expert search problem: searching experts on the web, where millions of web pages and thousands of names are considered. The two main issues are: Web pages might be of untrustworthy and have more noise; the knowledge evidences spotted in web pages are frequently unclear and ambiguous. The skilled search has been studied in different contexts, e.g., enterprises, academic communities. We propose to influence the huge quantity of co-occurrence information to calculate the significance and status of a person name for a query which is given. So this makes the recommendation system the most important and the trust worthiness of the system will be analyzed in the better way. The personalization will be depended based on the individual user process in the web search mainly worked in E-Commerce application.
Modeling User Expertise in Folksonomies by Fusing Multi-type Features
"... Abstract. The folksonomy refers to the online collaborative tagging system which offers a new open platform for content annotation with uncontrolled vocabulary. As folksonomies are gaining in popularity, the expert search and spammer detection in folksonomies attract more and more attention. Howeve ..."
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Abstract. The folksonomy refers to the online collaborative tagging system which offers a new open platform for content annotation with uncontrolled vocabulary. As folksonomies are gaining in popularity, the expert search and spammer detection in folksonomies attract more and more attention. However, most of previous work are limited on some folksonomy features. In this paper, we introduce a generic and flexible user expertise model for expert search and spammer detection. We first investigate a comprehensive set of expertise evidences related to users, objects and tags in folksonomies. Then we discuss the rich interactions between them and propose a unified Continuous CRF model to integrate these features and interactions. This model's applications for expert recommendation and spammer detection are also exploited. Extensive experiments are conducted on a real tagging dataset and demonstrate the model's advantages over previous methods, both in performance and coverage.
Web Mining Techniques for Query Log Analysis and Expertise Retrieval
, 2009
"... With the large increase in the amount of information available online, rich Web data can be obtained on the Internet, such as over one trillion Web pages, millions of scientific literature, and different interactions with society, like question answers, query logs. Currently, Web mining techniques h ..."
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With the large increase in the amount of information available online, rich Web data can be obtained on the Internet, such as over one trillion Web pages, millions of scientific literature, and different interactions with society, like question answers, query logs. Currently, Web mining techniques has emerged as an important research area to help Web users find their information need. In general, Web users express their information need as queries, and expect to obtain the needed information from the Web data through Web mining techniques. To better understand what users want in terms of the given query, it is very essential to analyze the query logs. On the other hand, the returned information may be Web pages, images, and other types of data. Beyond the traditional information, it would be quite interesting and important to identify relevant experts with expertise for further consulting about the query topic, which is also called expertise retrieval. The objective of this thesis is to establish automatic content analysis meth-ods and scalable graph-based models for query log analysis and expertise
