PR-Rank: A Parameter Regression Approach for Learning-to-Rank Model Adaptation Without Target Domain Data

Takumi Ito, Atsuki Maruta, Makoto P. Kato, Sumio Fujita

Abstract: This paper addresses a problem of constructing a Learning-to-Rank (LtR) model tailored to a target domain without using any domain-specific queries and relevance judgements. Our proposed method, PR-Rank, incorporates domain features, which are represented in a real-valued vector and can be estimated by domain experts, for adapting LtR models. The key component in our method is a parameter regression model that learns to regress the optimal parameters of the LtR model from the domain features. This eliminates the need for access to users’ queries and relevance judgements in a target domain, which is often unavailable in new and emerging services. In our experiments, we compared the performance of the proposed method against a domain-agnostic method, using publicly available LtR datasets including OHSUMED, MQ2007/2008, TREC Web track, and MSLR. The results showed that our method could outperform the baseline model trained on a large amount of data without considering domain differences.

External IDs:dblp:conf/wise/ItoMKF24