Building implicit links from content for forum search

Abstract

The objective of Web forums is to create a shared space for open communications and discussions of specific topics and issues. The tremendous information behind forum sites is not fully-utilized yet. Most links between forum pages are automatically created, which means the link-based ranking algorithm cannot be applied efficiently. In this paper, we proposed a novel ranking algorithm which tries to introduce the content information into link-based methods as implicit links. The basic idea is derived from the more focused random surfer: the surfer may more likely jump to a page which is similar to what he is reading currently. In this manner, we are allowed to introduce the content similarities into the link graph as a personalization bias. Our method, named Fine-grained Rank (FGRank), can be efficiently computed based on an automatically generated topic hierarchy. Not like the topic-sensitive PageRank, our method only need to compute single PageRank score for each page. Another contribution of this paper is to present a very efficient algorithm for automatically generating topic hierarchy and map each page in a large-scale collection onto the computed hierarchy. The experimental results show that the proposed method can improve retrieval performance, and reveal that content-based link graph is also important compared with the hyper-link graph.