Nowadays, search engine users commonly rely on query suggestions to improve
their initial inputs. Current systems are very good at recommending lexical
adaptations or spelling corrections to users' queries. However, they often
struggle to suggest semantically related keywords given a user's query. The
construction of a detailed query is crucial in some tasks, such as legal
retrieval or academic search. In these scenarios, keyword suggestion methods
are critical to guide the user during the query formulation. This paper
proposes two novel models for the keyword suggestion task trained on scientific
literature. Our techniques adapt the architecture of Word2Vec and FastText to
generate keyword embeddings by leveraging documents' keyword co-occurrence.
Along with these models, we also present a specially tailored negative sampling
approach that exploits how keywords appear in academic publications. We devise
a ranking-based evaluation methodology following both known-item and ad-hoc
search scenarios. Finally, we evaluate our proposals against the
state-of-the-art word and sentence embedding models showing considerable
improvements over the baselines for the tasks.

本文提出了两种新颖的模型来进行关键字建议任务，使用 Word2Vec 和 FastText 的架构，通过利用文档中的关键字共现来生成关键字嵌入向量，并采用特殊的负例抽样方法来利用关键字在学术出版物中的出现方式。此外，还提供了基于排名的评估方法，对所提出的模型进行评估，并在已知项目和自由搜索场景下显示了较大的性能改进。

查询建议的关键词嵌入

Keyword Embeddings for Query Suggestion

Advertising (ad for short) keyword suggestion is important for sponsored
search to improve online advertising and increase search revenue. There are two
common challenges in this task. First, the keyword bidding problem: hot ad
keywords are very expensive for most of the advertisers because more
advertisers are bidding on more popular keywords, while unpopular keywords are
difficult to discover. As a result, most ads have few chances to be presented
to the users. Second, the inefficient ad impression issue: a large proportion
of search queries, which are unpopular yet relevant to many ad keywords, have
no ads presented on their search result pages. Existing retrieval-based or
matching-based methods either deteriorate the bidding competition or are unable
to suggest novel keywords to cover more queries, which leads to inefficient ad
impressions. To address the above issues, this work investigates to use
generative neural networks for keyword generation in sponsored search. Given a
purchased keyword (a word sequence) as input, our model can generate a set of
keywords that are not only relevant to the input but also satisfy the domain
constraint which enforces that the domain category of a generated keyword is as
expected. Furthermore, a reinforcement learning algorithm is proposed to
adaptively utilize domain-specific information in keyword generation. Offline
evaluation shows that the proposed model can generate keywords that are
diverse, novel, relevant to the source keyword, and accordant with the domain
constraint. Online evaluation shows that generative models can improve coverage
(COV), click-through rate (CTR), and revenue per mille (RPM) substantially in
sponsored search.

该论文探讨了使用生成神经网络以及强化学习算法来解决在线广告推荐中的关键字提取问题，提出的模型能够生成与输入相关、多样化的关键字，并且满足领域特定信息的限制条件，并通过离线和在线评估表明，所提出的模型能够显著提高广告投放的覆盖率、点击率和 RPM。