The ever-growing computation capability distributed infrastructure brings tremendous opportunities for mining and analysis of data that was impossible otherwise. Meanwhile, the inherent computation model of distributed system also brings unique and non-trivial challenges for traditional Auto-ML, including the explosion of data dimensions, the expected absence of features, and the heterogeneity of information. This is especially the case in modern Internet enterprises, where data in the scale of trillions are stored in multiple data centers, and the discovery of subtle signals could incur significant impact in revenue and welfare. How can we best harness the large scale distributed machine learning, but without keeping engineers constantly in the loop? In this work, we present Hadoop-MTA, a system for Multi Data-center, Trillion Concepts, Auto-ML on top of the Hadoop distributed computation environment that leverages sparsity aware heterogeneous knowledge graph representation and dimensionality agnostic parallel learning. Through multiple large scale experiments, we find that Hadoop-MTA significantly output-performs competitive state of the art distributed learning algorithms and scales well to trillion scale data-sets. Our model is rolled out to Hadoop serving infrastructure in Yahoo covering billions of unique identities and shows improvements 129.5% accuracy and 106.5 % weighted F1-score (more than 2x) on key targeting use cases.

One of the primary component in ads creative recommendation system is the brand anonymization that removes brand-specific information from ad text for legal compliance and providing ready to use template for the advertisers to customize and consume. In our previous work [1] on ads creative recommendation system, the anonymization is done via a block list created solely based on manual reviewing, which is expensive and limits in the scale of the deployment of the ads recommendation. In this work we investigate a large scale, automated approach for brand anonymization. Such a problem presents many unique and non-trivial challenges, including the domain specificity of the brand entities, the fine-granularity requirements of structured output, the tight constraint of the limited contexts, the high level of grammatical noise in the advertisement data, and the heterogeneity of information required to perform anonymization. We propose a transformer model that leverage implicit knowledge together with a label-light adaptation procedure for this task. Our model is rolled out to ads systems in Yahoo that cover billions of impression traffic per month and improved previous production system by 68.3% F1-score on token level prediction and 61.6% on ad level prediction.

In this work we study the hierarchical organization of technical documents, where given a set of documents and a hierarchy of categories, the goal is to assign documents to their corresponding categories. Unlike prior work on supervised hierarchical document categorization that relies on large amount of labeled training data, which is expensive to obtain in closed technical domain and tends to stale as new knowledge emerges, we study this problem in a weak supervision setting, by leveraging semantic information from concepts. The core idea is to project both documents and categories into a common concept embedding space, where their fine-grained similarity can be easily and effectively computed. Experiments over real-world datasets from the subject of computer science, physics & mathematics, and medicine demonstrated the superior performance of our approach over a wide range of state of the art baseline approaches.

The number of scientific publications is ever increasing. The long time to digest a scientific paper posts great challenges on the number of papers people can read, which impedes a quick grasp of major activities in new research areas especially for intelligence analysts and novice researchers. To accelerate such a process, we first define a new problem called mining algorithm roadmap in scientific publications, and then propose a new weakly supervised method to build the roadmap. The algorithm roadmap describes evolutionary relation between different algorithms, and sketches the undergoing research and the dynamics of the area. It is a tool for analysts and researchers to locate the successors and families of algorithms when analyzing and surveying a research field. We first propose abbreviated words as candidates for algorithms and then use tables as weak supervision to extract these candidates and labels. Next we propose a new method called Cross-sentence Attention NeTwork for cOmparative Relation (CANTOR) to extract comparative algorithms from text. Finally, we derive order for individual algorithm pairs with time and frequency to construct the algorithm roadmap. Through comprehensive experiments, our proposed algorithm shows its superiority over the baseline methods on the proposed task.

In this work, we study the problem of concept mining, which serves as the first step in transforming unstructured text into structured information, and supports downstream analytical tasks such as information extraction, organization, recommendation and search. Previous work mainly relies on statistical signals, existing knowledge bases, or predefined linguistic patterns. In this work, we propose a novel approach that mines concepts based on their occurrence contexts, by learning embedding vector representations that summarize the context information for each possible candidates, and use these embeddings to evaluate the concept's global quality and their fitness to each local context. Experiments over several real-world corpora demonstrate the superior performance of our method. A publicly available implementation is provided at https://github.com/kleeeeea/ECON.

Most conventional document categorization methods require a large number of documents with labeled categories for training. These methods are hard to be applied in scenarios, such as scientific publications, where training data is expensive to obtain and categories could change over years and across domains. In this work, we propose UNEC, an unsupervised representation learning model that directly categories documents without the need of labeled training data. Specifically, we develop a novel cascade embedding approach. We first embed concepts, i.e., significant phrases mined from scientific publications, into continuous vectors, which capture concept semantics. Based on the concept similarity graph built from the concept embedding, we further embed concepts into a hidden category space, where the category information of concepts becomes explicit. Finally we categorize documents by jointly considering the category attribution of their concepts. Our experimental results show that UNEC significantly outperforms several strong baselines on a number of real scientific corpora, under both automatic and manual evaluation.

Existing work on knowledge discovery focuses on using natural language techniques to extract entities and relationships from textual documents. However, today relational tables are abundant in quantities, and are often well-structured with coherent data values. So far these rich relational tables have been largely overlooked for the purpose of knowledge discovery. In this work, we study the problem of building concept hierarchies using a large corpus of enterprise spreadsheet tables. Our method first groups distinct values from tables into a large hierarchical tre based on co-occurrence statistics. We then "summarize" the large tree by selecting important tree nodes that are likely good concepts based on how well they "describe" the original corpus. The result is a small concept hierarchy that is easy for humans to understand and curate. Our end-to-end algorithms are designed to run on Map-Reduce and to scale to large corpus. Experiments using real enterprise spreadsheet corpus show that proposed approach can generate concepts with high quality.

Online platforms, such as Meetup and Plancast, have recently become popular for planning gatherings and event organization. However, there is a surprising lack of studies on how to effectively and efficiently organize social events for a large group of people through such platforms. In this paper, we study the key computational problem involved in organization of social events, to our best knowledge, for the first time.

Recommender Systems (RS) play a vital role in applications such as e-commerce and on-demand content streaming. Research on RS has mainly focused on the customer perspective , i.e., accurate prediction of user preferences and maximization of user utilities. As a result, most existing techniques are not explicitly built for revenue maximization , the primary business goal of enterprises. In this work, we explore and exploit a novel connection between RS and the profitability of a business. As recommendations can be seen as an information channel between a business and its customers, it is interesting and important to investigate how to make strategic dynamic recommendations leading to maximum possible revenue. To this end, we propose a novel revenue model that takes into account a variety of factors including prices, valuations, saturation effects, and competition amongst products. Under this model, we study the problem of finding revenue-maximizing recommendation strategies over a finite time horizon. We show that this problem is NP-hard, but approximation guarantees can be obtained for a slightly relaxed version, by establishing an elegant connection to matroid theory. Given the prohibitively high complexity of the approximation algorithm, we also design intelligent heuristics for the original problem. Finally, we conduct extensive experiments on two real and synthetic datasets and demonstrate the efficiency, scalability, and effectiveness our algorithms, and that they significantly outperform several intuitive baselines.