Online Learning to Rank for Cross-Language Information Retrieval

Razieh Rahimi, Azadeh Shakery
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
Online learning to rank for information retrieval has shown great promise in optimization of Web search results based on user interactions. However, online learning to rank has been used only in the monolingual se ing where queries and documents are in the same language. In this work, we present the rst empirical study of optimizing a model for Cross-Language Information Retrieval (CLIR) based on implicit feedback inferred from user interactions. We show that ranking models for CLIR with
more » ... ble performance can be learned in an online se ing, although ranking features are noisy because of the language mismatch. CCS CONCEPTS •Information systems →Users and interactive retrieval; Learning to rank; Multilingual and cross-lingual retrieval; KEYWORDS Online learning; Learning to rank; Cross-language information retrieval
doi:10.1145/3077136.3080710 dblp:conf/sigir/RahimiS17 fatcat:rxujxfnszvh2thabwe5yln7n4i