Query expansion for personalized cross-language information retrieval

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.