基于稀疏和可审查用户简档的个性化实体搜索
Personalized Entity Search by Sparse and Scrutable User Profiles
摘要：
之前关于个性化网络搜索结果的工作集中在考虑查询和点击日志来捕获用户的个人兴趣。对于产品搜索，已经利用了关于购买和评级的广泛用户历史。然而，对于一般的实体搜索，例如针对特定主题的书籍或具有某些功能的旅游目的地，个性化在很大程度上未被探索。在本文中，我们解决个性化的图书搜索，作为一个典型的情况下，实体搜索，利用稀疏的用户简档通过在线问卷调查。我们设计并比较了各种基于语言模型或神经学习的重新排序方法。我们的实验表明，即使是非常稀疏的个人信息可以提高搜索结果的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
