Generative Retrieval introduces a new approach to Information Retrieval by reframing it as a constrained generation task, leveraging recent advancements in Autoregressive (AR) language models. However, AR-based Generative Retrieval methods suffer from high inference latency and cost compared to traditional dense retrieval techniques, limiting their practical applicability. This paper investigates fully Non-autoregressive (NAR) language models as a more efficient alternative for generative retrieval. While standard NAR models alleviate latency and cost concerns, they exhibit a significant drop in retrieval performance (compared to AR models) due to their inability to capture dependencies between target tokens. To address this, we question the conventional choice of limiting the target token space to solely words or sub-words. We propose PIXNAR, a novel approach that expands the target vocabulary of NAR models to include multi-word entities and common phrases (up to 5 million tokens), thereby reducing token dependencies. PIXNAR employs inference optimization strategies to maintain low inference latency despite the significantly larger vocabulary. Our results demonstrate that PIXNAR achieves a relative improvement of 31.0% in MRR@10 on MS MARCO and 23.2% in Hits@5 on Natural Questions compared to standard NAR models with similar latency and cost. Furthermore, online A/B experiments on a large commercial search engine show significant increase in clicks and revenue.

Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representations for data points and labels has attracted much attention due to its superiority over earlier XC methods that used sparse, hand-crafted features. Negative mining techniques have emerged as a critical component of all deep XC methods, allowing them to scale to millions of labels. However, despite recent advances, training deep XC models with large encoder architectures such as transformers remains challenging. This paper notices that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down. In response, this paper introduces NGAME, a light-weight mini-batch creation technique that offers provably accurate in-batch negative samples. This allows training with larger mini-batches offering significantly faster convergence and higher accuracies than existing negative sampling techniques. NGAME was found to be up to 16% more accurate than state-of-the-art methods on a wide array of benchmark datasets for extreme classification, as well as 3% more accurate at retrieving search engine queries in response to a user webpage visit to show personalized ads. In live A/B tests on a popular search engine, NGAME yielded up to 23% gains in click-through-rates. Code for NGAME is available at https://github.com/Extreme-classification/ngame

Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set. This paper develops the DeepXML framework that addresses these challenges by decomposing the deep extreme multi-label task into four simpler sub-tasks each of which can be trained accurately and efficiently. Choosing different components for the four sub-tasks allows DeepXML to generate a family of algorithms with varying trade-offs between accuracy and scalability. In particular, DeepXML yields the Astec algorithm that could be 2-12% more accurate and 5-30x faster to train than leading deep extreme classifiers on publically available short text datasets. Astec could also efficiently train on Bing short text datasets containing up to 62 million labels while making predictions for billions of users and data points per day on commodity hardware. This allowed Astec to be deployed on the Bing search engine for a number of short text applications ranging from matching user queries to advertiser bid phrases to showing personalized ads where it yielded significant gains in click-through-rates, coverage, revenue and other online metrics over state-of-the-art techniques currently in production. DeepXML's code is available at https://github.com/Extreme-classification/deepxml.

This paper develops the GalaXC algorithm for Extreme Classification, where the task is to annotate a document with the most relevant subset of labels from an extremely large label set. Extreme classification has been successfully applied to several real world web-scale applications such as web search, product recommendation, query rewriting, etc. GalaXC identifies two critical deficiencies in leading extreme classification algorithms. First, existing approaches generally assume that documents and labels reside in disjoint sets, even though in several applications, labels and documents cohabit the same space. Second, several approaches, albeit scalable, do not utilize various forms of metadata offered by applications, such as label text and label correlations. To remedy these, GalaXC presents a framework that enables collaborative learning over joint document-label graphs at massive scales, in a way that naturally allows various auxiliary sources of information, including label metadata, to be incorporated. GalaXC also introduces a novel label-wise attention mechanism to meld high-capacity extreme classifiers with its framework. An efficient end-to-end implementation of GalaXC is presented that could be trained on a dataset with 50M labels and 97M training documents in less than 100 hours on 4 × V100 GPUs. This allowed GalaXC to not only scale to applications with several millions of labels, but also be up to 18% more accurate than leading deep extreme classifiers, while being upto 2-50 × faster to train and 10 × faster to predict on benchmark datasets. GalaXC is particularly well-suited to warm-start scenarios where predictions need to be made on data points with partially revealed label sets, and was found to be up to 25% more accurate than extreme classification algorithms specifically designed for warm start settings. In A/B tests conducted on the Bing search engine, GalaXC could improve the Click Yield (CY) and coverage by 1.52% and 1.11% respectively. Code for GalaXC is available at https://github.com/Extreme-classification/GalaXC

Content discovery is fast becoming the preferred tool for user engagement on the web. Discovery allows users to get educated and entertained about their topics of interest. StumbleUpon is the largest personalized content discovery engine on the Web, delivering more than 1 billion personalized recommendations per month. As a recommendation system one of the primary metrics we track is whether the user returns (retention) to use the product after their initial experience (session) with StumbleUpon.

Computational advertising refers to finding the most relevant ads matching a particular context on the web. The core problem attacked in computational advertising CA is of the match making between the ads and the context. My research work aims at leveraging various user interaction, ad and advertiser related information and contextual information for improving the relevance, ranking and targeting of ads. The research work focuses on the identification of various factors that contribute in retrieving and ranking the most relevant set of ads that match best with the context. Specifically, information associated with the user, publisher and advertiser is leveraged for this purpose.

Contextual Advertising (CA) refers to the placement of ads that are contextually related to the web page content. The science of CA deals with the task of finding advertising keywords from web pages. We present a different candidate selection method to extract advertising keywords from a web page. This method makes use of Part-of-Speech (POS) patterns that restrict the number of potential candidates a classifier has to handle. It fetches words/phrases that belong to the selected set of POS patterns. We design four systems based on chunking method and the features they use. These systems are trained on a naive Bayes classifier with a set of web pages annotated with 'advertising' keywords. The systems can then find advertising keywords from previously unseen web pages. Empirical evaluation shows that systems using the proposed chunking method perform better than the systems using N-Gram based chunking. All improvements in the systems are found statistically significant at a 99% confidence interval.

Ads on the search engine (SE) are generally ranked based on their Click-through rates (CTR). Hence, accurately predicting the CTR of an ad is of paramount importance for maximizing the SE's revenue. We present a model that inherits the click information of rare/new ads from other semantically related ads. The semantic features are derived from the query ad click-through graphs and advertisers account information. We show that the model learned using these features give a very good prediction for the CTR values.