Text-visual (or called semantic-visual) embedding is a central problem in
vision-language research. It typically involves mapping of an image and a text
description to a common feature space through a CNN image encoder and a RNN
language encoder. In this paper, we propose a new method for learning
text-visual embedding using both image titles and click-through data from an
image search engine. We also propose a new triplet loss function by modeling
positive awareness of the embedding, and introduce a novel mini-batch-based
hard negative sampling approach for better data efficiency in the learning
process. Experimental results show that our proposed method outperforms
existing methods, and is also effective for real-world text-to-visual
retrieval.

论文提出一种新的方法，使用图像标题和来自图像搜索引擎的点击数据来学习文本 - 视觉嵌入，并通过建模嵌入的积极感知提出新的三元损失函数，以及引入一种新的基于小批次的难例负采样方法来提高学习过程的数据效率，实验结果表明，该方法的表现优于现有方法，并且对于现实世界的文本到视觉检索也十分有效。

使用标题和点击数据的多任务文本到视觉嵌入

Multitask Text-to-Visual Embedding with Titles and Clickthrough Data

Retrieval models such as CLSM is trained on click-through data which treats
each clicked query-document pair as equivalent. While training on click-through
data is reasonable, this paper argues that it is sub-optimal because of its
noisy and long-tail nature (especially for sponsored search). In this paper, we
discuss the impact of incorporating or disregarding the long tail pairs in the
training set. Also, we propose a weighing based strategy using which we can
learn semantic representations for tail pairs without compromising the quality
of retrieval. We conducted our experiments on Bing sponsored search and also on
Amazon product recommendation to demonstrate that the methodology is domain
agnostic.
Online A/B testing on live search engine traffic showed improvements in
clicks (11.8\% higher CTR) and as well as improvement in quality (8.2\% lower
bounce rate) when compared to the unweighted model. We also conduct the
experiment on Amazon Product Recommendation data where we see slight
improvements in NDCG Scores calculated by retrieving among co-purchased
product.

本文研究基于点击数据的检索模型，探讨在训练集中引入或忽略长尾查询 - 文档对的影响，提出了一种基于加权的策略用于学习长尾语义表示，并在必应赞助搜索和亚马逊产品推荐上进行实验。在线 A/B 测试结果显示，相比于不加权模型，点击率提高了 11.8％，反弹率降低了 8.2％，同时在亚马逊产品推荐数据上也略有提高。

权重学习：一种针对广告检索的代价敏感方法

Learning From Weights: A Cost-Sensitive Approach For Ad Retrieval

Ranking is a central task in machine learning and information retrieval. In
this task, it is especially important to present the user with a slate of items
that is appealing as a whole. This in turn requires taking into account
interactions between items, since intuitively, placing an item on the slate
affects the decision of which other items should be placed alongside it. In
this work, we propose a sequence-to-sequence model for ranking called
seq2slate. At each step, the model predicts the next `best' item to place on
the slate given the items already selected. The sequential nature of the model
allows complex dependencies between the items to be captured directly in a
flexible and scalable way. We show how to learn the model end-to-end from weak
supervision in the form of easily obtained click-through data. We further
demonstrate the usefulness of our approach in experiments on standard ranking
benchmarks as well as in a real-world recommendation system.

该篇研究论文介绍了一种基于序列的排名模型 seq2slate，可以结合物品之间的相互影响进行新物品的推荐，并利用易于获取的点击数据进行端到端模型训练，在标准排名基准测试和真实推荐系统中获得了实验结果的验证。