Web search is an experience that naturally lends itself to recommendations, including query suggestions and related entities. In this article, we propose to recommend specific tasks to users, based on their search queries, such as planning a holiday trip or organizing a party. Specifically, we introduce the problem of query-based task recommendation and develop methods that combine well-established term-based ranking techniques with continuous semantic representations, including sentence representations from several transformer-based models. Using a purpose-built test collection, we find that our method is able to significantly outperform a strong text-based baseline. Further, we extend our approach to using a set of queries that all share the same underlying task, referred to as search mission, as input. The study is rounded off with a detailed feature and query analysis.

中文翻译：

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根据搜索查询和任务推荐任务

Web 搜索是一种自然有助于推荐的体验，包括查询建议和相关实体。在本文中，我们建议根据用户的搜索查询向他们推荐特定任务，例如计划假期旅行或组织聚会。具体来说，我们介绍了基于查询的任务推荐问题，并开发了将成熟的基于术语的排名技术与连续语义表示相结合的方法，包括来自几个基于转换器的模型的句子表示。使用专门构建的测试集合，我们发现我们的方法能够显着优于基于文本的强大基线。此外，我们将我们的方法扩展为使用一组查询，这些查询都共享相同的基础任务，称为搜索任务，作为输入。