  To help individuals express themselves better, quotation recommendation is
receiving growing attention. Nevertheless, most prior efforts focus on modeling
quotations and queries separately and ignore the relationship between the
quotations and the queries. In this work, we introduce a transformation matrix
that directly maps the query representations to quotation representations. To
better learn the mapping relationship, we employ a mapping loss that minimizes
the distance of two semantic spaces (one for quotation and another for
mapped-query). Furthermore, we explore using the words in history queries to
interpret the figurative language of quotations, where quotation-aware
attention is applied on top of history queries to highlight the indicator
words. Experiments on two datasets in English and Chinese show that our model
outperforms previous state-of-the-art models.
