An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity, e.g. a book, with a search engine is that the entity is retrievable, i.e. there are queries for which the system will surface such entity in the top results. However, machine-learned search engines have a high retrievability bias, where the majority of the queries return the same entities. This happens partly due to the predominance of narrow intent queries, where users create queries using the title of an already known entity, e.g. in book search 'harry potter'. The amount of broad queries where users want to discover new entities, e.g. in music search 'chill lyrical electronica with an atmospheric feeling to it', and have a higher tolerance to what they might find, is small in comparison. We focus here on two factors that have a negative impact on the retrievability of the entities (I) the training data used for dense retrieval models and (II) the distribution of narrow and broad intent queries issued in the system. We propose CtrlQGen, a method that generates queries for a chosen underlying intent-narrow or broad. We can use CtrlQGen to improve factor (I) by generating training data for dense retrieval models comprised of diverse synthetic queries. CtrlQGen can also be used to deal with factor (II) by suggesting queries with broader intents to users. Our results on datasets from the domains of music, podcasts, and books reveal that we can significantly decrease the retrievability bias of a dense retrieval model when using CtrlQGen. First, by using the generated queries as training data for dense models we make 9% of the entities retrievable (go from zero to non-zero retrievability). Second, by suggesting broader queries to users, we can make 12% of the entities retrievable in the best case.

翻译：在线平台的重要目标之一是促进内容发现，即让用户能够找到他们原本不熟悉的目录实体。要在搜索引擎中发现一个实体（如图书），前提是该实体具有可检索性，即存在某些查询，使得系统能将该实体呈现在搜索结果前列。然而，机器学习驱动的搜索引擎存在严重的可检索性偏差，即大多数查询都会返回相同的实体。这一现象部分源于窄意图查询的主导地位：用户使用已知实体的标题构建查询（例如图书搜索中的“哈利·波特”）。相比之下，用户希望通过宽查询发现新实体（例如音乐搜索中的“带有氛围感的冷感抒情电子乐”），并且对搜索结果有更高容忍度的查询数量较少。我们重点关注对实体可检索性产生负面影响的两个因素：（I）用于密集检索模型的训练数据；（II）系统中窄意图与宽意图查询的分布。我们提出CtrlQGen方法，能够为指定的底层意图（窄意图或宽意图）生成查询。CtrlQGen可通过生成由多样化合成查询构成的训练数据来改进因素（I），也可通过向用户推荐更宽意图的查询来处理因素（II）。在音乐、播客和图书领域数据集上的实验结果表明，使用CtrlQGen可显著降低密集检索模型的可检索性偏差：首先，将生成的查询作为密集模型的训练数据时，9%的实体变得可检索（即可检索性从零变为非零）；其次，在最佳情况下，通过向用户推荐更宽泛的查询，可使12%的实体实现可检索性。