Multi-View Learning for Web Spam Detection
This work addresses the problem of efficiently and accurately detecting spam pages for search engines, representing an incremental improvement over existing methods.
The paper tackled web spam detection by proposing a multi-view learning approach that combines outputs from base classifiers using distinct feature sets, resulting in a 22% improvement in AUC and linear speedup for parallel execution.
Spam pages are designed to maliciously appear among the top search results by excessive usage of popular terms. Therefore, spam pages should be removed using an effective and efficient spam detection system. Previous methods for web spam classification used several features from various information sources (page contents, web graph, access logs, etc.) to detect web spam. In this paper, we follow page-level classification approach to build fast and scalable spam filters. We show that each web page can be classified with satisfiable accuracy using only its own HTML content. In order to design a multi-view classification system, we used state-of-the-art spam classification methods with distinct feature sets (views) as the base classifiers. Then, a fusion model is learned to combine the output of the base classifiers and make final prediction. Results show that multi-view learning significantly improves the classification performance, namely AUC by 22%, while providing linear speedup for parallel execution.