Xiao Bai

Generating a set of keyphrases that convey the main concepts discussed in a document has been applied to improve various applications including document retrieval and online advertising. The state-of-the-art approaches mostly rely on the neural sequence-to-sequence framework to generate keyphrases. However, training such deep neural networks either requires a significant amount of human efforts in obtaining ground truth keyphrases or suffers from lower quality training data derived from weakly supervised signals. More recently, pre-trained language models are fine-tuned to build more data-efficient keyphrase generation models. Yet, the documents often need to be truncated to adapt to the pre-trained context window. On the other hand, large language models (LLMs) have demonstrated impressive abilities in understanding very long text and generating answers for a wide range of natural language processing tasks, making them great candidates for improving keyphrase generation. There however is a lack of a systematic study on how to use LLMs, especially in an industrial setting that requires low generation latency. In this work, we present an empirical study to facilitate a more informed use of LLMs for keyphrase generation. We compare zero-shot and few-shot in-context learning with parameter efficient fine-tuning using a number of open-source LLMs. We show that using only a handful of well selected human annotated samples, the LLMs already outperform the fine-tuned language model baselines. When thousands of human labeled samples are available, fine-tuned large language models significantly improve the amount and the quality of the generated keyphrases. To enable efficient keyphrase generation at scale, we distill the knowledge from LLMs to a base-size language model. Our evaluation shows significant increase in user reach when the generated keyphrases are used for contextual targeting at Yahoo.

Web page categorization has been extensively studied in the literature and has been successfully used to improve information retrieval, recommendation, personalization and ad targeting. With the new industry trend of not tracking users’ online behavior without their explicit permission, using contextual targeting to accurately understand web pages in order to display ads that are topically relevant to the pages becomes more important. This is challenging, however, because an ad request only contains the URL of a web page. As a result, there is very limited available text for making accurate classifications. In this paper, we propose a unified multilingual model that can seamlessly classify web pages in 5 high-impact languages using either their full content or just their URLs with limited text. We adopt multiple data sampling techniques to increase coverage for rare categories in our training corpus, and modify the loss using class-based re-weighting to smooth the influence of frequent versus rare categories. We also propose using an ensemble of teacher models for knowledge distillation and explore different ways to create a teacher ensemble. Offline evaluation shows at least 2.6% improvement in mean average precision across 5 languages compared to a URL classification model trained with single-teacher knowledge distillation. The unified model for both full-content and URL-only input further improves the mean average precision of the dedicated URL classification model by 0.6%. We launched the proposed models, which achieve at least 37% better mean average precision than the legacy tree-based models, for contextual targeting in the Yahoo Demand Side Platform, leading to a significant ad delivery and revenue increase.

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. By comparing the two models, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. We also show the importance of the position feature in the proposed approaches in improving the prediction accuracy. We also show the potential of leveraging the CTR prediction of the proposed deep learning models for query-ad relevance modeling and query-ad matching tasks in sponsored search.

This article presents P4Q, a fully decentralized gossip-based protocol to personalize query processing in social tagging systems. P4Q dynamically associates each user with social acquaintances sharing similar tagging behaviors. Queries are gossiped among such acquaintances, computed on-the-fly in a collaborative, yet partitioned manner, and results are iteratively refined and returned to the querier. Analytical and experimental evaluations convey the scalability of P4Q for top- k query processing, as well its inherent ability to cope with users updating profiles and departing.