I've been reading a paper about ranking in folksonomy, called FolkRank, proposed in Trend detection in folksonomies, Information Retrieval in Folksonomies: Search and Ranking.
FolkRank is based on well-known PageRank algorithm used in Google (The Anatomy of a Large-Scale Hypertextual Web Search Engine and The PageRank Citation Ranking: Bringing Order to the Web).
In a nut shell, the basic idea of PageRank is that a page is important if there are many pages linking to it. This can be formulated by the following matrix equation:
R(u) = c \sum R(v)/N or R(u) = c \sum R(v)/N + cE(u)
where
u is a web page and v is a page referring u,
R(u) and R(v) are PageRank of u and v repectively,
N is a number of all links in page u, and
E(u) is called a source of rank.
Folksonomy can also be represented as a graph by using the similar concept of PageRank but a few exceptions:
- Folksonomy graph is undirected, while web graph in PageRank is directed.
- Nodes are heterogeneous and the graph is triadic, while in PageRank the nodes are homogeneous.
I think it's a good start to begin with applying PageRank algorithm to folksonomy recommendation system. For this purpose, I'm planing to dig into more details on the PageRank algorithm and find some sample code for better understand.
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