CWRCzech: 100M Query-Document Czech Click Dataset and Its Application to Web Relevance Ranking

TL;DR

CWRCzech is the largest Czech click dataset with 100 million query-document pairs, enabling improved relevance ranking models that outperform human-annotated data by leveraging large-scale user behavior data.

Contribution

The paper introduces CWRCzech, the largest Czech click dataset, and demonstrates how large-scale user behavior data can enhance relevance ranking models.

Findings

Models trained on large-scale user data outperform those trained on human annotations.

CWRCzech provides extensive click and dwell time data for Czech search queries.

The dataset is publicly available for research use.

Abstract

We present CWRCzech, Click Web Ranking dataset for Czech, a 100M query-document Czech click dataset for relevance ranking with user behavior data collected from search engine logs of Seznam$.$cz. To the best of our knowledge, CWRCzech is the largest click dataset with raw text published so far. It provides document positions in the search results as well as information about user behavior: 27.6M clicked documents and 10.8M dwell times. In addition, we also publish a manually annotated Czech test for the relevance task, containing nearly 50k query-document pairs, each annotated by at least 2 annotators. Finally, we analyze how the user behavior data improve relevance ranking and show that models trained on data automatically harnessed at sufficient scale can surpass the performance of models trained on human annotated data. CWRCzech is published under an academic non-commercial license and is available to the research community at https://github.com/seznam/CWRCzech.