Queries with similar information needs tend to have similar document clicks,
especially in biomedical literature search engines where queries are generally
short and top documents account for most of the total clicks. Motivated by
this, we present a novel architecture for biomedical literature search, namely
Log-Augmented DEnse Retrieval (LADER), which is a simple plug-in module that
augments a dense retriever with the click logs retrieved from similar training
queries. Specifically, LADER finds both similar documents and queries to the
given query by a dense retriever. Then, LADER scores relevant (clicked)
documents of similar queries weighted by their similarity to the input query.
The final document scores by LADER are the average of (1) the document
similarity scores from the dense retriever and (2) the aggregated document
scores from the click logs of similar queries. Despite its simplicity, LADER
achieves new state-of-the-art (SOTA) performance on TripClick, a recently
released benchmark for biomedical literature retrieval. On the frequent (HEAD)
queries, LADER largely outperforms the best retrieval model by 39% relative
NDCG@10 (0.338 v.s. 0.243). LADER also achieves better performance on the less
frequent (TORSO) queries with 11% relative NDCG@10 improvement over the
previous SOTA (0.303 v.s. 0.272). On the rare (TAIL) queries where similar
queries are scarce, LADER still compares favorably to the previous SOTA method
(NDCG@10: 0.310 v.s. 0.295). On all queries, LADER can improve the performance
of a dense retriever by 24%-37% relative NDCG@10 while not requiring additional
training, and further performance improvement is expected from more logs. Our
regression analysis has shown that queries that are more frequent, have higher
entropy of query similarity and lower entropy of document similarity, tend to
benefit more from log augmentation.

LADER 是一种简单的插件模块，可将稠密检索器与类似训练查询的点击日志相结合，从而实现生物医学文献检索的最新技术表现。

LADER: 生物医学文献检索的对数增强稠密检索

LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search

Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from
biased user click logs. Most of the current ULTR methods are based on the
examination hypothesis (EH), which assumes that the click probability can be
factorized into two scalar functions, one related to ranking features and the
other related to bias factors. Unfortunately, the interactions among features,
bias factors and clicks are complicated in practice, and usually cannot be
factorized in this independent way. Fitting click data with EH could lead to
model misspecification and bring the approximation error.
In this paper, we propose a vector-based EH and formulate the click
probability as a dot product of two vector functions. This solution is complete
due to its universality in fitting arbitrary click functions. Based on it, we
propose a novel model named Vectorization to adaptively learn the relevance
embeddings and sort documents by projecting embeddings onto a base vector.
Extensive experiments show that our method significantly outperforms the
state-of-the-art ULTR methods on complex real clicks as well as simple
simulated clicks.

本文提出一种基于向量的解法，用于构建与点击、偏差和排序特征等相关联的、不受限制的点击概率模型，并基于此方法提出了一种名为统计向量化的自适应学习排序模型，实验证明其表现优于现有的无偏学习方法。

标量不足以描述：基于向量化的无偏学习排序

Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank

Click logs are valuable resources for a variety of information retrieval (IR)
tasks. This includes query understanding/analysis, as well as learning
effective IR models particularly when the models require large amounts of
training data. We release a large-scale domain-specific dataset of click logs,
obtained from user interactions of the Trip Database health web search engine.
Our click log dataset comprises approximately 5.2 million user interactions
collected between 2013 and 2020. We use this dataset to create a standard IR
evaluation benchmark -- TripClick -- with around 700,000 unique free-text
queries and 1.3 million pairs of query-document relevance signals, whose
relevance is estimated by two click-through models. As such, the collection is
one of the few datasets offering the necessary data richness and scale to train
neural IR models with a large amount of parameters, and notably the first in
the health domain. Using TripClick, we conduct experiments to evaluate a
variety of IR models, showing the benefits of exploiting this data to train
neural architectures. In particular, the evaluation results show that the best
performing neural IR model significantly improves the performance by a large
margin relative to classical IR models, especially for more frequent queries.

该研究发布了一份从 2013 年至 2020 年间收集的大规模特定领域点击日志数据集，用于培训神经信息检索模型。通过使用该数据集，研究人员甚至发现使用神经信息检索模型可以显着提高信息检索的性能，尤其是对于经常查询的查询。

TripClick：一家大型健康网站的日志文件

TripClick: The Log Files of a Large Health Web Search Engine

Users of Web search engines reveal their information needs through queries
and clicks, making click logs a useful asset for information retrieval.
However, click logs have not been publicly released for academic use, because
they can be too revealing of personally or commercially sensitive information.
This paper describes a click data release related to the TREC Deep Learning
Track document corpus. After aggregation and filtering, including a k-anonymity
requirement, we find 1.4 million of the TREC DL URLs have 18 million
connections to 10 million distinct queries. Our dataset of these queries and
connections to TREC documents is of similar size to proprietary datasets used
in previous papers on query mining and ranking. We perform some preliminary
experiments using the click data to augment the TREC DL training data, offering
by comparison: 28x more queries, with 49x more connections to 4.4x more URLs in
the corpus. We present a description of the dataset's generation process,
characteristics, use in ranking and suggest other potential uses.

本文介绍了一个与 TREC 深度学习赛文献语料库相关的点击数据发布，通过初步实验，利用点击数据扩充 TREC DL 训练数据的效果达到了较大改善，该数据集的生成过程、特点、在排名中的应用以及其他潜在应用均有描述。