Web page classification is an important application in many fields of Internet information retrieval,
such as providing directory classiˉcation and vertical search. Methods based on query log which is a light weight
version of Web page classiˉcation can avoid Web content crawling, making it relatively high in e±ciency, but
the sparsity of user click data makes it di±cult to be used directly for constructing a classifier. To solve this
problem, we explore the semantic relations among di?erent queries through word embedding, and propose three
improved graph structure classification algorithms. To re°ect the semantic relevance between queries, we map
the user query into the low-dimensional space according to its query vector in the ˉrst step. Then, we calculate
the uniform resource locator (URL) vector according to the relationship between the query and URL. Finally, we
use the improved label propagation algorithm (LPA) and the bipartite graph expansion algorithm to classify the
unlabeled Web pages. Experiments show that our methods make about 20% more increase in F1-value than other
Web page classification methods based on query log.
YE Feiyue (叶飞跃), MA Yixing (马祎星)
. Research on Web Page Classification Method Based on Query Log[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(3)
: 404
.
DOI: 10.1007/s12204-017-1899-0
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