Difference between revisions of "Zhang et all, WWW 2007"
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* HITS Authority : Similar to [[UsesMethod::HITS]] algorithm where good hub is a user who is helped by many experts and good authority is a user who helps many good hubs. | * HITS Authority : Similar to [[UsesMethod::HITS]] algorithm where good hub is a user who is helped by many experts and good authority is a user who helps many good hubs. | ||
| − | + | In experiments the authors used Spearman's Rho and Kendall's Tau measures to understand the correlations between these ranking algorithms and the human-assigned ratings. It has been observed that they are highly correlated which means that structural information can be used to identify experts in online communities. | |
| + | |||
| + | It has been also observed that algorithms like PageRank and HITS which works really well in WWW, does not outperform simpler algorithms used in this online community. | ||
| + | |||
| + | Therefore understanding the characteristics | ||
| + | |||
[[RelatedPaper::Arguello et al, ICWSM 2008]] and [[RelatedPaper::Elsas et al, TREC 2007]]. | [[RelatedPaper::Arguello et al, ICWSM 2008]] and [[RelatedPaper::Elsas et al, TREC 2007]]. | ||
Revision as of 20:32, 1 April 2011
Citation
Online version
Summary
The aim of this paper is to identify users with high expertise within online expertise-sharing communities. This expertise finding system uses graph-based algorithms on social networks within the community.
They treat expertise as a relative concept.
network based algorithms such as PageRank, HITS
They created a post-reply network in which each user is represented as a node and a directed edge is created from each user who started the post to other users who replied to it. The prestige measure of this network is highly correlated with a user's expertise due to the way the network is constructed. Therefore this network is called community expertise network (CEN).
Network Characteristics
The authors experimented on the Java Forum which is a large online help-seeking community. Before testing the algorithms they did several analysis to characterize the network. Below are the performed analysis and their results
- The Bow tie structure analysis : More than half of the users only asks questions. 13% only answers and 12% both answers and asks.
- Degree distribution analysis : The majority of users answers only a few questions but few active users answers a lot of questions.
- Degree correlation analysis : Top repliers answer questions for everyone but less expert users do not reply to high expert users.
It is important to note that these characteristics are different from WWW graphs.
Expertise Ranking Algorithms
- Simple statistical measures : Just counting the number of replies or counting the number of users helped to calculate the score of expertise of a user.
- Z-score : A measure that combines one's asking and replying patterns.
- Expertise Rank Algorithm : PageRank like algorithm which uses not only count of users helped but also whom one helped.
- HITS Authority : Similar to HITS algorithm where good hub is a user who is helped by many experts and good authority is a user who helps many good hubs.
In experiments the authors used Spearman's Rho and Kendall's Tau measures to understand the correlations between these ranking algorithms and the human-assigned ratings. It has been observed that they are highly correlated which means that structural information can be used to identify experts in online communities.
It has been also observed that algorithms like PageRank and HITS which works really well in WWW, does not outperform simpler algorithms used in this online community.
Therefore understanding the characteristics
