Abstract
Cross-language information retrieval research has favored system-centered approaches in the past. The user is not an integral part of the translation and retrieval processes. In this paper, we investigate the problem of personalized cross-language information retrieval by exploiting query expansion techniques. The original query is augmented with terms mined from the user's historical usage information in one language, with the aim of retrieving more relevant results in another language. Experiments semi-automatically constructed by using bilingual Wikipedia documents showed that in general personalized approaches work better than non-personalized approaches. We also found that an individual user model generated from one language can be used to enhance the personalized cross-language information retrieval.