词条 | Draft:FolkRank |
释义 |
FolkRank is a ranking algorithm for folksonomies on social bookmarking systems (for example, Flickr, del.icio.us, and bibsonomy). It adapted the PageRank algorithm to the folksonomies' structure. FolkRank was introduced by Andreas Hotho in 2006[1]. It is applied not only to determining an overall ranking and specific topic-related rankings, but also to a variety of applications, finding communities, structuring search results and trend detection within folksonomy, to name a few. IntroductionIt is simply easy for users to bookmark the resources on social resource sharing tools and use tags to describe or organize their bookmarks. The collection of all each user's assignment is called a personomy. Within folksonomies, the users are able to explore their own personomy as well as the personomies of other users. By clicking on resources or tags, they can see which tags others assign to the same resource with them, who use the same tags with them and which resources are connected to these tags. However, Over the past ten years, folksonomies have acquired a large number of users who have been creating a huge amount of information. This causes users more difficult to seek information by browsing within a folksonomy. Moreover, most of users are more familiar with search interfaces than browsing interfaces. Therefore, employing standard techniques for searching information in folksonomy-based systems help users ease the information seeking process. The issue here is how to provide a suitable ranking list to a user query in order to meet their information need. Fortunately, the structure of folksonomy can be transformed into a graph structure. Hotho[1] took advantages of the folksonomy structure and adapted PageRank algorithm to FolkRank algorithm in order to rank users, tags and resources in folksonomy based systems. The algorithm can be used for a topic-specific ranking. This ranking also can help to detect trends in these systems and recommend items (e.g., users, resources) to a specific user based on their preference. Formal definitions of a folksonomySeveral studies have worked with folksonomy and defined a structure for it to use in their applications. All of them represent the folksonomy structure in some form that describes the connections among users, tags and resources. Hotho and colleagues[2] give a formal definitions for a folksonomy as follows:
Although these definitions above focus on a folksonomy with users, tags and resources as three dimensions, it is possible to enhance the structure to include more dimensions [3]. For example, Wu et al. [4]consider the fourth element: timestamps which are assigned to tag-resource pairs in order to consider temporal aspects in their analysis. AlgorithmThe FolkRank algorithm computes a topic-specific ranking in a folksonomy, it operates on an undirected, tripartite graph. The PageRank weight-spreading approach can not be directly applied to folksonomy because the structure of folksonomy is different with web graph. Therefore, a folksonomy structure F = (U, T, R, Y), firstly, is converted into a tripartite undirected hypergraph G = (V, E), which connects users, tags, and resources.
The PageRank formula can be iteratively applied to this hypergraph as follows: where:
Preference weights need to be specified for the preference vector p in order to compute a ranking of tags, resources and/or users tailored to the preferred item/topic. In web search, the tags which can be referred as search query terms receive a higher weight compared to the remaining items (i.e., remaining tags, all users and all resources) whose weight scores are equally distributed. With any distributions of weights, the equation {{Math|1={{!}}{{!}}w{{!}}{{!}}1 = {{!}}{{!}}p{{!}}{{!}}1}}. The algorithm is presented as follows. Algorithm: FolkRank Input: Undirected, tripartite graph {{math|GF}} , a randomly chosen baseline vector {{Math|w0}} and a randomly chosen FolkRank vector {{Math|w1}}. 1. Set preference vector p. 2. Compute baseline vector {{Math|w0}} with p = 1 and . 3. Compute topic specific vector {{Math|w1}} specific preference vector p. 4. w := {{Math|w1}} - {{Math|w0}} is the final weight vector. Output: FolkRank vector w. Applications of FolkRank
See also
References1. ^1 {{cite news| title = FolkRank: A Ranking Algorithm for Folksonomies.| url = http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.5271&rep=rep1&type=pdf| first1 = Andreas| last1 = Hotho| first2 = Robert| last2 = Jaschke| first3 = Christoph| last3 = Schmitz| first4 = Gerd| last4 = Stumme| work = Berlin: De Gruyter Saur| year = 2006}} 2. ^{{cite news| title = Information Retrieval in Folksonomies: Search and Ranking.| url = https://link.springer.com/chapter/10.1007/11762256_31| first1 = Andreas| last1 = Hotho| first2 = Robert| last2 = Jäschke| first3 = Christoph| last3 = Schmitz| first4 = Gerd| last4 = Stumme| work = The Semantic Web: Research and Applications Book| year = 2006}} 3. ^{{cite news| title = Contextualization, user modeling and personalization in the social web: from social tagging via context to cross-system user modeling and personalization.| url = http://edok01.tib.uni-hannover.de/edoks/e01dh11/660718537.pdf| first1 = Fabian| last1 = Abel| work = PhD thesis, University of Hanover| year = 2011}} 4. ^{{cite news| title = Exploring social annotations for the semantic web.| url = http://doi.acm.org/10.1145/1135777.1135839| first1 = Xian| last1 = Wu| first2 = Lei| last2 = Zhang| first3 = Yong| last3 = Yu| work = In WWW ’06: Proceedings of the 15th international conference on World Wide Web| year = 2011}} 5. ^{{cite news| title = Trend detection in folksonomies.| url = https://pdfs.semanticscholar.org/1380/dc0c83c44581c93f16d52ba4757339947796.pdf| first1 = Andreas| last1 = Hotho| first2 = Robert| last2 = Jaschke| first3 = Christoph| last3 = Schmitz| work = PhD thesis, University of Hanover| year = 2011}} 6. ^{{cite news| title = Social tagging recommender systems.| url = https://dx.doi.org/10.1007/978-0-387-85820-3_19| first1 = Leandro Balby| last1 = Marinho| first2 = Alexandros| last2 = Nanopoulos| first3 = Lars| last3 = Schmidt-Thieme| first4 = Robert| last4 = Jäschke| first5 = Andreas| last5 = Hotho| first6 = Gerd| last6 = Stumme| first7 = Panagiotis| last7 = Symeonidis| work = Recommender Systems Handbook| year = 2011}} 7. ^{{cite news| title = Logsonomy – social information retrieval with logdata.| url = http://dl.acm.org/citation.cfm?id=1379123| first1 = Beate| last1 = Krause| first2 = Robert| last2 = Jäschke| first3 = Andreas| last3 = Hotho| first4 = Gerd| last4 = Stumme| work = Proceedings of the nineteenth ACM conference on Hypertext and hypermedia| year = 2008}} |
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