An Improved Centroid-Based Approach for Multi-label Classification of Web Pages by Genre

With the explosive expansion of information on the world wide web, search engines are becoming more significant in the day-to-day lives of humans. Even though a search engine generally gives huge number of results for certain query, the majority of the search engine users simply view the first few web pages in result lists. Consequently, the ranking position has become a most important concern of internet service providers. This article addresses the vulnerabilities, spamming attacks, and countermeasures in blogging sites. In the first part, the article explores the spamming types and detailed section on vulnerabilities. In the next part, an attack scenario of form spamming is presented, and defense approach is presented. Consequently, the aim of this article is to provide review of vulnerabilities, threats of spamming associated with blogging websites, and effective measures to counter them.

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Functional Classification of Web Pages with Deep Learning

2019 27th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2019.8806240 ◽

2019 ◽

Cited By ~ 2

Author(s):

Caner Balim ◽

Kemal Ozkan

Keyword(s):

Deep Learning ◽

Web Pages ◽

Functional Classification

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Automatic Classification of Tibetan Web Pages

2012 International Conference on Computer Science and Electronics Engineering ◽

10.1109/iccsee.2012.177 ◽

2012 ◽

Cited By ~ 1

Author(s):

Guixian Xu ◽

Chuncheng Xiang ◽

Xu Gao ◽

Xiaobing Zhao ◽

Guosheng Yang

Keyword(s):

Automatic Classification ◽

Web Pages

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A Method of Subtopic Classification of Search Engine Suggests by Integrating a Topic Model and Word Embeddings

International Journal of Software Innovation ◽

10.4018/ijsi.2018070105 ◽

2018 ◽

Vol 6 (3) ◽

pp. 67-78

Author(s):

Tian Nie ◽

Yi Ding ◽

Chen Zhao ◽

Youchao Lin ◽

Takehito Utsuro

Keyword(s):

Search Engine ◽

Information Needs ◽

Web Search ◽

Topic Model ◽

Japanese Version ◽

Word Embedding ◽

Coarse Grained ◽

Web Pages ◽

Word Embeddings

The background of this article is the issue of how to overview the knowledge of a given query keyword. Especially, the authors focus on concerns of those who search for web pages with a given query keyword. The Web search information needs of a given query keyword is collected through search engine suggests. Given a query keyword, the authors collect up to around 1,000 suggests, while many of them are redundant. They classify redundant search engine suggests based on a topic model. However, one limitation of the topic model based classification of search engine suggests is that the granularity of the topics, i.e., the clusters of search engine suggests, is too coarse. In order to overcome the problem of the coarse-grained classification of search engine suggests, this article further applies the word embedding technique to the webpages used during the training of the topic model, in addition to the text data of the whole Japanese version of Wikipedia. Then, the authors examine the word embedding based similarity between search engines suggests and further classify search engine suggests within a single topic into finer-grained subtopics based on the similarity of word embeddings. Evaluation results prove that the proposed approach performs well in the task of subtopic classification of search engine suggests.

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