Conditional Sequential Slate Optimization
Yipeng Zhang, Mingjian Lu, Saratchandra Indrakanti, Manojkumar, Rangasamy Kannadasan, Abraham Bagherjeiran

TL;DR
This paper introduces Conditional Sequential Slate Optimization (CSSO), a hybrid framework that improves search result ranking by optimizing for relevance and distributional criteria, addressing inter-document dependencies and business objectives.
Contribution
The paper presents CSSO, a novel method extending traditional slate optimization to jointly optimize relevance and distributional constraints in search results.
Findings
CSSO outperforms existing ranking methods in adhering to distributional criteria.
CSSO maintains or improves relevance metrics compared to baseline methods.
Experiments on public and e-commerce datasets validate CSSO's effectiveness.
Abstract
The top search results matching a user query that are displayed on the first page are critical to the effectiveness and perception of a search system. A search ranking system typically orders the results by independent query-document scores to produce a slate of search results. However, such unilateral scoring methods may fail to capture inter-document dependencies that users are sensitive to, thus producing a sub-optimal slate. Further, in practice, many real-world applications such as e-commerce search require enforcing certain distributional criteria at the slate-level, due to business objectives or long term user retention goals. Unilateral scoring of results does not explicitly support optimizing for such objectives with respect to a slate. Hence, solutions to the slate optimization problem must consider the optimal selection and order of the documents, along with adherence to…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Advanced Image and Video Retrieval Techniques · Recommender Systems and Techniques
