Query Representation with Global Consistency on User Click Graph

TL;DR

This paper introduces a global consistency model for query representation that leverages the global property of URLs, such as inverse URL frequency, to improve upon traditional click graph methods, demonstrating superior performance on AOL data.

Contribution

The paper proposes a novel global consistency model utilizing inverse URL frequency for query representation, addressing limitations of local click frequency-based methods.

Findings

The global consistency model outperforms existing models on AOL search data.

Inverse URL frequency effectively captures global URL properties.

The approach improves query representation accuracy.

Abstract

Extensive research has been conducted on query log analysis. A query log is generally represented as a bipartite graph on a query set and a URL set. Most of the traditional methods used the raw click frequency to weigh the link between a query and a URL on the click graph. In order to address the disadvantages of raw click frequency, researchers proposed the entropy-biased model, which incorporates raw click frequency with inverse query frequency of the URL as the weighting scheme for query representation. In this paper, we observe that the inverse query frequency can be considered a global property of the URL on the click graph, which is more informative than raw click frequency, which can be considered a local property of the URL. Based on this insight, we develop the global consistency model for query representation, which utilizes the click frequency and the inverse query frequency of a URL in a consistent manner. Furthermore, we propose a new scheme called inverse URL frequency as an effective way to capture the global property of a URL. Experiments have been conducted on the AOL search engine log data. The result shows that our global consistency model achieved better performance than the current models.