  Bilingual lexicon induction induces the word translations by aligning
independently trained word embeddings in two languages. Existing approaches
generally focus on minimizing the distances between words in the aligned pairs,
while suffering from low discriminative capability to distinguish the relative
orders between positive and negative candidates. In addition, the mapping
function is globally shared by all words, whose performance might be hindered
by the deviations in the distributions of different languages. In this work, we
propose a novel ranking-oriented induction model RAPO to learn personalized
mapping function for each word. RAPO is capable of enjoying the merits from the
unique characteristics of a single word and the cross-language isomorphism
simultaneously. Extensive experimental results on public datasets including
both rich-resource and low-resource languages demonstrate the superiority of
our proposal. Our code is publicly available in
\url{https://github.com/Jlfj345wf/RAPO}.
