Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search

Grbovic, Mihajlo; Djuric, Nemanja; Radosavljević, Vladan; Silvestri, Fabrizio; Bhamidipati, Narayan

doi:10.1145/2766462.2767709

Cited by 84 publications

(54 citation statements)

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“…We saw word2vec enter wider adoption in IR research (e.g., Ganguly et al 2015;Grbovic et al 2015a;Kenter and de Rijke 2015;Zheng and Callan 2015;Zuccon et al 2015), as well as a flourishing of neural IR work appearing at SIGIR (Ganguly et al 2015;Grbovic et al 2015b;Mitra 2015;Severyn and Moschitti 2015;Vulic and Moens 2015;Zheng and Callan 2015), spanning ad-hoc search (Ganguly et al 2015;Vulic and Moens 2015;Zheng and Callan 2015;Zuccon et al 2015), QA sentence selection and Twitter reranking (Kenter and de Rijke 2015), cross-lingual IR (Vulic and Moens 2015), paraphrase detection (Kenter and de Rijke 2015), query completion (Mitra 2015), query suggestion (Sordoni et al 2015), and sponsored search (Grbovic et al 2015a, b). In 2015, we also saw the first workshop on Neural IR.…”

Section: A Brief History Of Neural Irmentioning

confidence: 99%

“…Besides the content, temporal context is useful for defining TTU contexts. For instance, the context of the query can be defined by other queries in the same session (Grbovic et al 2015b). Finally, the context of a document is defined by joining its content and the neighboring documents in a document stream in Djuric et al (2015).…”

Section: Learnmentioning

confidence: 99%

“…In their subsequent, longer study, Grbovic et al (2015b) propose a method to train context and content-aware word embeddings. The first proposal is context2vec.…”

Section: Learn To Predictmentioning

confidence: 99%

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Neural information retrieval: at the end of the early years

et al. 2017

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A recent ''third wave'' of neural network (NN) approaches now delivers state-ofthe-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper,

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Section: A Brief History Of Neural Irmentioning

confidence: 99%

Section: Learnmentioning

confidence: 99%

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Neural information retrieval: at the end of the early years

et al. 2017

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“…To the best of our knowledge, there is no previous work adopting semantic-level text features for the purpose of click prediction, in particular word embeddings to measure query-ad relevance. In a similar vein of research, Grbovic et al (2015) adopted word embeddings to the task of query rewriting for a better match between queries and keywords that advertisers entered into an auction. Using the embeddings, semantically similar queries are mapped into vectors close in the embedding space, which allows expansion of a query via K-nearest neighbor search.…”

Section: Related Workmentioning

confidence: 99%

Joint Embedding of Query and Ad by Leveraging Implicit Feedback

Lee

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Sponsored search is at the center of a multibillion dollar market established by search technology. Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities. Lexical features derived from the text of both the query and ads play a significant role, complementing features based on historical click information. The purpose of this paper is to explore the use of word embedding techniques to generate effective text features that can capture not only lexical similarity between query and ads but also the latent user intents. We identify several potential weaknesses of the plain application of conventional word embedding methodologies for ad click prediction. These observations motivated us to propose a set of novel joint word embedding methods by leveraging implicit click feedback. We verify the effectiveness of these new word embedding models by adding features derived from the new models to the click prediction system of a commercial search engine. Our evaluation results clearly demonstrate the effectiveness of the proposed methods. To the best of our knowledge this work is the first successful application of word embedding techniques for the task of click prediction in sponsored search.

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“…In this session we will focus on semantic matching settings where a supervised signal is available. The signal can be explicit, such as a label for learning task-speci c latent representations [25-27, 36, 47, 48], or relevance labels and, more implicitly, clicks for neural IR methods [15,19,31,41,42]. …”

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confidence: 99%

Neural Networks for Information Retrieval

Kenter¹,

Borisov

Gysel

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modernday research has given rise to many di erent approaches for many di erent IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they bene t IR research. It covers key architectures, as well as the most promising future directions. MOTIVATIONPrompted by the advances of deep learning in computer vision research, neural networks have resurfaced as a popular machine learning paradigm in many other directions of research as well, including information retrieval. Recent years have seen neural networks being applied to all key parts of the typical modern IR pipeline, such core ranking algorithms [26,42,51], click models [9,10], knowledge graphs [8,35], text similarity [28,47], entity retrieval [52,53], language modeling [5], question answering [22,56], and dialogue systems [34,54].A key advantage that sets neural networks apart from many learning strategies employed earlier, is their ability to work from raw input data. E.g., when given enough training data, well-designed networks can become feature extractors themselves, e.g., incorporating basic input characteristics such as term frequency (tf) and term saliency (idf)-that used to be pre-calculated o ine-in their initial layers. Where designing features used to be a crucial aspect and contribution of newly proposed IR approaches, the focus * Corresponding author.SIGIR '17, Shinjuku, Tokyo, Japan has shifted to designing network architectures instead. As a consequence, many di erent architectures and paradigms have been proposed, such as auto-encoders, recursive networks, recurrent networks, convolutional networks, various embedding methods, deep reinforcement and deep q-learning, and, more recently, generative adversarial networks, of which most have been applied in IR settings. The aim of the neural networks for IR (NN4IR) tutorial is to provide a clear overview of the main network architectures currently applied in IR and to show explicitly how they relate to previous work. The tutorial covers methods applied in industry and academia, with in-depth insights into the underlying theory, core IR tasks, applicability, key assets and handicaps, scalability concerns and practical tips and tricks.We expect the tutorial to be useful both for academic and industrial researchers and practitioners who either want to develop new neural models, use them in their own research in other areas or apply the models described here to improve actual IR systems. OBJECTIVESThe material in the tutorial covers a broad range of IR applications. It is structured as follows:Preliminaries (60 minutes). The rece...

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Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search

Cited by 84 publications

References 27 publications

Neural information retrieval: at the end of the early years

Neural information retrieval: at the end of the early years

Joint Embedding of Query and Ad by Leveraging Implicit Feedback

Neural Networks for Information Retrieval

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