In contrast to traditional exhaustive search, selective search first clusters
documents into several groups before all the documents are searched
exhaustively by a query, to limit the search executed within one group or only
a few groups. Selective search is designed to reduce the latency and
computation in modern large-scale search systems. In this study, we propose
MICO, a Mutual Information CO-training framework for selective search with
minimal supervision using the search logs. After training, MICO does not only
cluster the documents, but also routes unseen queries to the relevant clusters
for efficient retrieval. In our empirical experiments, MICO significantly
improves the performance on multiple metrics of selective search and
outperforms a number of existing competitive baselines.

本论文提出了 MICO 互信息协同训练框架，使用搜索日志进行有监督的选择性搜索，可将文档聚类，并将未知的查询路由到相关群集以进行有效检索。在多种指标上，MICO 显著提高了选择性搜索的性能，并胜过了一些现有的竞争基线。

MICO: 基于相互信息协同训练的有选择性搜索

MICO: Selective Search with Mutual Information Co-training

Query understanding plays a key role in exploring users' search intents and
facilitating users to locate their most desired information. However, it is
inherently challenging since it needs to capture semantic information from
short and ambiguous queries and often requires massive task-specific labeled
data. In recent years, pre-trained language models (PLMs) have advanced various
natural language processing tasks because they can extract general semantic
information from large-scale corpora. Therefore, there are unprecedented
opportunities to adopt PLMs for query understanding. However, there is a gap
between the goal of query understanding and existing pre-training strategies --
the goal of query understanding is to boost search performance while existing
strategies rarely consider this goal. Thus, directly applying them to query
understanding is sub-optimal. On the other hand, search logs contain user
clicks between queries and urls that provide rich users' search behavioral
information on queries beyond their content. Therefore, in this paper, we aim
to fill this gap by exploring search logs. In particular, to incorporate search
logs into pre-training, we first construct a query graph where nodes are
queries and two queries are connected if they lead to clicks on the same urls.
Then we propose a novel graph-enhanced pre-training framework, GE-BERT, which
can leverage both query content and the query graph. In other words, GE-BERT
can capture both the semantic information and the users' search behavioral
information of queries. Extensive experiments on various query understanding
tasks have demonstrated the effectiveness of the proposed framework.

本文介绍了如何利用搜索日志构建查询图和使用 GE-BERT 框架来同时捕获查询的语义信息和用户搜索行为信息，以改进查询理解任务。

图增强 BERT 用于查询理解

Graph Enhanced BERT for Query Understanding

Search is an integral part of a software development process. Developers
often use search engines to look for information during development, including
reusable code snippets, API understanding, and reference examples. Developers
tend to prefer general-purpose search engines like Google, which are often not
optimized for code related documents and use search strategies and ranking
techniques that are more optimized for generic, non-code related information.
In this paper, we explore whether a general purpose search engine like Google
is an optimal choice for code-related searches. In particular, we investigate
whether the performance of searching with Google varies for code vs. non-code
related searches. To analyze this, we collect search logs from 310 developers
that contains nearly 150,000 search queries from Google and the associated
result clicks. To differentiate between code-related searches and non-code
related searches, we build a model which identifies the code intent of queries.
Leveraging this model, we build an automatic classifier that detects a code and
non-code related query. We confirm the effectiveness of the classifier on
manually annotated queries where the classifier achieves a precision of 87%, a
recall of 86%, and an F1-score of 87%. We apply this classifier to
automatically annotate all the queries in the dataset. Analyzing this dataset,
we observe that code related searching often requires more effort (e.g., time,
result clicks, and query modifications) than general non-code search, which
indicates code search performance with a general search engine is less
effective.

本文探讨使用通用搜索引擎 (Google) 进行与代码相关的搜索是否是最佳选择。为了分析这个问题，我们从 310 个开发人员收集含有近 150,000 个搜索查询的搜索日志，并基于一个模型将查询进行分类。我们发现与通用非代码搜索相比，代码相关的搜索通常需要更多的努力 (例如时间、结果点击和查询修改)，这表明通用搜索引擎的代码搜索性能不如专门的搜索工具。

评估开发者如何使用通用网络搜索进行代码检索

Evaluating How Developers Use General-Purpose Web-Search for Code  Retrieval

LinkedIn is the largest professional network with more than 350 million
members. As the member base increases, searching for experts becomes more and
more challenging. In this paper, we propose an approach to address the problem
of personalized expertise search on LinkedIn, particularly for exploratory
search queries containing {\it skills}. In the offline phase, we introduce a
collaborative filtering approach based on matrix factorization. Our approach
estimates expertise scores for both the skills that members list on their
profiles as well as the skills they are likely to have but do not explicitly
list. In the online phase (at query time) we use expertise scores on these
skills as a feature in combination with other features to rank the results. To
learn the personalized ranking function, we propose a heuristic to extract
training data from search logs while handling position and sample selection
biases. We tested our models on two products - LinkedIn homepage and LinkedIn
recruiter. A/B tests showed significant improvements in click through rates -
31% for CTR@1 for recruiter (18% for homepage) as well as downstream messages
sent from search - 37% for recruiter (20% for homepage). As of writing this
paper, these models serve nearly all live traffic for skills search on LinkedIn
homepage as well as LinkedIn recruiter.

本文提出了一种使用基于矩阵分解的协同过滤方法解决 LinkedIn 上基于技能的个性化专家搜索问题的方法，并在实际测试中取得了较好的成果。