  Prior work on personalizing web search results has focused on considering
query-and-click logs to capture users individual interests. For product search,
extensive user histories about purchases and ratings have been exploited.
However, for general entity search, such as for books on specific topics or
travel destinations with certain features, personalization is largely
underexplored. In this paper, we address personalization of book search, as an
exemplary case of entity search, by exploiting sparse user profiles obtained
through online questionnaires. We devise and compare a variety of re-ranking
methods based on language models or neural learning. Our experiments show that
even very sparse information about individuals can enhance the effectiveness of
the search results.
