English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The goal of unbiased learning to rank (ULTR) is to leverage implicit userfeedback for optimizing learning-to-rank systems. Among existing solutions,automatic ULTR algorithms that jointly learn user bias models (i.e., propensitymodels) with unbiased rankers have received a lot of attention due to theirsuperior performance and low deployment cost in practice. Despite theirtheoretical soundness, the effectiveness is usually justified under a weaklogging policy, where the ranking model can barely rank documents according totheir relevance to the query. However, when the logging policy is strong, e.g.,an industry-deployed ranking policy, the reported effectiveness cannot bereproduced. In this paper, we first investigate ULTR from a causal perspectiveand uncover a negative result: existing ULTR algorithms fail to address theissue of propensity overestimation caused by the query-document relevanceconfounder. Then, we propose a new learning objective based on backdooradjustment and highlight its differences from conventional propensity models,which reveal the prevalence of propensity overestimation. On top of that, weintroduce a novel propensity model called Logging-Policy-aware Propensity (LPP)model and its distinctive two-step optimization strategy, which allows for thejoint learning of LPP and ranking models within the automatic ULTR framework,and actualize the unconfounded propensity estimation for ULTR. Extensiveexperiments on two benchmarks demonstrate the effectiveness andgeneralizability of the proposed method.

Unconfounded Propensity Estimation for Unbiased Ranking