Tagging has been recognized as a successful practice to boost relevance
matching for information retrieval (IR), especially when items lack rich
textual descriptions. A lot of research has been done for either multi-label
text categorization or image annotation. However, there is a lack of published
work that targets at item tagging specifically for IR. Directly applying a
traditional multi-label classification model for item tagging is sub-optimal,
due to the ignorance of unique characteristics in IR. In this work, we propose
to formulate item tagging as a link prediction problem between item nodes and
tag nodes. To enrich the representation of items, we leverage the query logs
available in IR tasks, and construct a query-item-tag tripartite graph. This
formulation results in a TagGNN model that utilizes heterogeneous graph neural
networks with multiple types of nodes and edges. Different from previous
research, we also optimize both full tag prediction and partial tag completion
cases in a unified framework via a primary-dual loss mechanism. Experimental
results on both open and industrial datasets show that our TagGNN approach
outperforms the state-of-the-art multi-label classification approaches.

通过构建一个查询 - 项目 - 标签三元组图和优化完整标签预测和部分标签完成两种情况的标签预测问题，我们提出了一种基于异构图神经网络的 Item Tagging 模型，证明在信息检索方面，该模型的性能优于现有的多标签分类方法。

面向信息检索的项目标记：基于三分图神经网络的方法

Item Tagging for Information Retrieval: A Tripartite Graph Neural  Network based Approach

Interactive tools make data analysis more efficient and more accessible to
end-users by hiding the underlying query complexity and exposing interactive
widgets for the parts of the query that matter to the analysis. However,
creating custom tailored (i.e., precise) interfaces is very costly, and
automated approaches are desirable. We propose a syntactic approach that uses
queries from an analysis to generate a tailored interface. We model interface
widgets as functions I(q) -> q' that modify the current analysis query $q$, and
interfaces as the set of queries that its widgets can express. Our system,
Precision Interfaces, analyzes structural changes between input queries from an
analysis, and generates an output interface with widgets to express those
changes. Our experiments on the Sloan Digital Sky Survey query log suggest that
Precision Interfaces can generate useful interfaces for simple unanticipated
tasks, and our optimizations can generate interfaces from logs of up to 10,000
queries in <10s.

本文提出了一种基于句法的方法，利用分析过程中的查询来生成定制化的界面，其中界面部件被建模为修改当前分析查询的函数，我们的系统可以从 10,000 个查询的日志中生成界面，并可用于简单的预测任务。

从查询日志中挖掘精准接口

Mining Precision Interfaces From Query Logs

Recent research has shown that the performance of search personalization
depends on the richness of user profiles which normally represent the user's
topical interests. In this paper, we propose a new embedding approach to
learning user profiles, where users are embedded on a topical interest space.
We then directly utilize the user profiles for search personalization.
Experiments on query logs from a major commercial web search engine demonstrate
that our embedding approach improves the performance of the search engine and
also achieves better search performance than other strong baselines.

本文针对搜索个性化提出一种新的嵌入式方法，利用用户的专业兴趣空间对用户进行嵌入和学习，实现对用户兴趣的表达和搜索个性化，并在商业网页搜索引擎的查询日志实验中，证明本方法的性能比其他强基线模型更好。