Combination of Multiple Bipartite Ranking for Web Content Quality Evaluation

TL;DR

This paper introduces a novel multi-partite web content quality estimation method by combining multiple bipartite ranking models with new encoding and weighting strategies, outperforming previous approaches.

Contribution

It presents a new approach using multiple bipartite ranking models with ternary and binary encoding, and predefined/adaptive weighting for improved web content quality estimation.

Findings

Binary coding with predefined weighting achieves the best performance.

The proposed method outperforms the best results in the ECML/PKDD 2010 Discovery Challenge.

Combining multiple bipartite models enhances ranking accuracy.

Abstract

Web content quality estimation is crucial to various web content processing applications. Our previous work applied Bagging + C4.5 to achive the best results on the ECML/PKDD Discovery Challenge 2010, which is the comibination of many point-wise rankinig models. In this paper, we combine multiple pair-wise bipartite ranking learner to solve the multi-partite ranking problems for the web quality estimation. In encoding stage, we present the ternary encoding and the binary coding extending each rank value to $L - 1$ (L is the number of the different ranking value). For the decoding, we discuss the combination of multiple ranking results from multiple bipartite ranking models with the predefined weighting and the adaptive weighting. The experiments on ECML/PKDD 2010 Discovery Challenge datasets show that \textit{binary coding} + \textit{predefined weighting} yields the highest performance in all four combinations and furthermore it is better than the best results reported in ECML/PKDD 2010 Discovery Challenge competition.