OpenRaiser/Intern-Atlas huggingface dataset:
Managing Risk of Bidding in Display Advertising
Authors: Haifeng Zhang, Weinan Zhang, Yifei Rong, Kan Ren, Wenxin Li, Jun Wang
Venue: WSDM
Year: 2017

In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies.
Mining Medical Causality for Diagnosis Assistance
Authors: Sendong Zhao
Venue: WSDM
Year: 2017

In the medical context, causal knowledge usually refers to causal relations between diseases and symptoms, living habits and diseases, symptoms which get better and therapy, drugs and side-effects, etc [3]. All these causal relations are usually in medical literature, forum and clinical cases and compose the core part of medical diagnosis. Therefore, mining these causal knowledge to predict disease and recommend therapy is of great value for assisting patients and professionals.
Representation Learning with Pair-wise Constraints for Collaborative Ranking
Authors: Fuzhen Zhuang, Dan Luo, Nicholas Jing Yuan, Xing Xie, Qing He
Venue: WSDM
Year: 2017

Last decades have witnessed a vast amount of interest and research in recommendation systems. Collaborative filtering, which uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users, is one of the most successful approaches to build recommendation systems. Most previous collaborative filtering approaches employ the matrix factorization techniques to learn latent user feature profiles and item feature profiles. Also many subsequent works are proposed to incorporate users' social network information and items' attributions to further improve recommendation performance under the matrix factorization framework. However, the matrix factorization based methods may not make full use of the rating information, leading to unsatisfying performance. Recently deep learning has been approved to be able to find good representations in natural language processing, image classification, and so on. Along this line, we propose a collaborative ranking framework via representation learning with pair-wise constraints (REAP for short), in which autoencoder is used to simultaneously learn the latent factors of both users and items and pair-wise ranked loss defined by (user, item) pairs is considered. Extensive experiments are conducted on five data sets to demonstrate the effectiveness of the proposed framework.
On the Power of Massive Text Data
Authors: Jiawei Han
Venue: WSDM
Year: 2018

The real-world big data is largely unstructured, dynamic, and interconnected, in the form of natural language text. It is highly desirable to transform such massive unstructured data into structured knowledge. Many researchers and practitioners rely on labor-intensive labeling and curation to extract knowledge from unstructured text data. However, such approaches may not be scalable to web-scale or adaptable to new domains, especially considering that a lot of text corpora are highly dynamic and domain-specific. We argue that massive text data itself contains a large body of hidden patterns, structures, and knowledge. Equipped with domain-independent and domain-specific knowledge-bases, a promising direction is to develop more systematic data mining methods to turn massive unstructured text data into structured knowledge. We introduce a set of methods developed recently in our own group on exploration of the power of big text data, including mining quality phrases using unsupervised, weakly supervised and distantly supervised approaches, recognition and typing of entities and relations by distant supervision, meta-pattern-based entity-attribute-value extraction, set expansion and local embedding-based multi-faceted taxonomy discovery, allocation of text documents into multi-dimensional text cubes, construction of heterogeneous information networks from text cube, and eventually mining multi-dimensional structured knowledge from massive text data. We show that massive text data itself can be powerful at disclosing patterns, structures and hidden knowledge, and it is promising to explore the power of massive, interrelated text data for transforming such unstructured data into structured knowledge.
Multi-Dimensional Network Embedding with Hierarchical Structure
Authors: Yao Ma, Zhaochun Ren, Ziheng Jiang, Jiliang Tang, Dawei Yin
Venue: WSDM
Year: 2018

Information networks are ubiquitous in many applications. A popular way to facilitate the information in a network is to embed the network structure into low-dimension spaces where each node is represented as a vector. The learned representations have been proven to advance various network analysis tasks such as link prediction and node classification. The majority of existing embedding algorithms are designed for the networks with one type of nodes and one dimension of relations among nodes. However, many networks in the real-world complex systems have multiple types of nodes and multiple dimensions of relations. For example, an e-commerce network can have users and items, and items can be viewed or purchased by users, corresponding to two dimensions of relations. In addition, some types of nodes can present hierarchical structure. For example, authors in publication networks are associated to affiliations; and items in e-commerce networks belong to categories. Most of existing methods cannot be naturally applicable to these networks. In this paper, we aim to learn representations for networks with multiple dimensions and hierarchical structure. In particular, we provide an approach to capture independent information from each dimension and dependent information across dimensions and propose a framework MINES, which performs Multi-dImension Network Embedding with hierarchical Structure. Experimental results on a network from a real-world e-commerce website demonstrate the effectiveness of the proposed framework.
Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data
Authors: Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman
Venue: WSDM
Year: 2018

We investigate how Simpson»s paradox affects analysis of trends in social data. According to the paradox, the trends observed in data that has been aggregated over an entire population may be different from, and even opposite to, those of the underlying subgroups. Failure to take this effect into account can lead analysis to wrong conclusions. We present a statistical method to automatically identify Simpson»s paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups. We apply the approach to data from Stack Exchange, a popular question-answering platform, to analyze factors affecting answerer performance, specifically, the likelihood that an answer written by a user will be accepted by the asker as the best answer to his or her question. Our analysis confirms a known Simpson»s paradox and identifies several new instances. These paradoxes provide novel insights into user behavior on Stack Exchange.
Influence Maximization in Online Social Networks
Authors: Çigdem Aslay, Laks V. S. Lakshmanan, Wei Lu, Xiaokui Xiao
Venue: WSDM
Year: 2018

Starting with the earliest studies showing that the spread of new trends, information, and innovations is closely related to the social influence exerted on people by their social networks, the research on social influence theory took off, providing remarkable evidence on social influence induced viral phenomena. Fueled by the extreme popularity of online social networks and social media, computational social influence has emerged as a subfield of data mining whose goal is to analyze and optimize social influence using computational frameworks such as algorithm design and theoretical modeling. One of the fundamental problems in this field is the problem of influence maximization, primarily motivated by the application of viral marketing. The objective is to identify a small set of users in a social network who, when convinced to adopt a product, shall influence others in the network in a manner that leads to a large number of adoptions.
A Call to Arms: Embrace Assistive AI Systems!
Authors: Andrei Z. Broder
Venue: WSDM
Year: 2018

A quarter-century ago Web search stormed the world: within a few years the Web search box became a standard tool of daily life ready to satisfy informational, transactional, and navigational queries needed for some task completion. However, two recent trends are dramatically changing the box»s role: first, the explosive spread of smartphones brings significant computational resources literally into the pockets of billions of users; second, recent technological advances in machine learning and artificial intelligence, and in particular in speech processing led to the wide deployment of assistive AI systems, culminating in personal digital assistants. Along the way, the "Web search box" has become an "assistance request box" (implicit, in the case of voice-activated assistants) and likewise, many other information processing systems (e.g. e-mail, navigation, personal search, etc) have adopted assistive aspects.
Neural Graph Learning: Training Neural Networks Using Graphs
Authors: Thang D. Bui, Sujith Ravi, Vivek Ramavajjala
Venue: WSDM
Year: 2018

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely Neural Graph Machines, that can combine the power of neural networks and label propagation. This work generalises previous literature on graph-augmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a)~allowing the network to train using labeled data as in the supervised setting, (b)~biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks.
Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams
Authors: Marc Bury, Chris Schwiegelshohn, Mara Sorella
Venue: WSDM
Year: 2018

Recommender systems are an integral part of many web applications. With increasingly larger user bases, scalability has become an important issue. Many of the most scalable algorithms with respect to both space and running times are based on locality sensitive hashing. However, a significant drawback is that these methods are only able to handle insertions to user profiles and tend to perform poorly when items may be removed. We initiate the study of scalable locality sensitive hashing (LSH) for dynamic input. Specifically, using the Jaccard index as similarity measure, we design (1) a sketching algorithm for similarity estimation via a black box reduction to $\ell_0$ norm estimation and (2) a locality sensitive hashing scheme maintainable in fully dynamic data streams that quickly filters out low-similarity pairs. Our algorithms have little to no overhead in terms of running time compared to previous LSH approaches for the insertion only case, and drastically outperform previous algorithms in case of deletions.
Putting Data in the Driver's Seat: Optimizing Earnings for On-Demand Ride-Hailing
Authors: Harshal A. Chaudhari, John W. Byers, Evimaria Terzi
Venue: WSDM
Year: 2018

On-demand ride-hailing platforms like Uber and Lyft are helping reshape urban transportation, by enabling car owners to become drivers for hire with minimal overhead. Although there are many studies that consider ride-hailing platforms holistically, e.g., from the perspective of supply and demand equilibria, little emphasis has been placed on optimization for the individual, self-interested drivers that currently comprise these fleets. While some individuals drive opportunistically either as their schedule allows or on a fixed schedule, we show that strategic behavior regarding when and where to drive can substantially increase driver income. In this paper, we formalize the problem of devising a driver strategy to maximize expected earnings, describe a series of dynamic programming algorithms to solve these problems under different sets of modeled actions available to the drivers, and exemplify the models and methods on a large scale simulation of driving for Uber in NYC. In our experiments, we use a newly-collected dataset that combines the NYC taxi rides dataset along with Uber API data, to build time-varying traffic and payout matrices for a representative six-month time period in greater NYC. From this input, we can reason about prospective itineraries and payoffs. Moreover, the framework enables us to rigorously reason about and analyze the sensitivity of our results to perturbations in the input data. Among our main findings is that repositioning throughout the day is key to maximizing driver earnings, whereas »chasing surge' is typically misguided and sometimes a costly move.
Connectivity in Complex Networks: Measures, Inference and Optimization
Authors: Chen Chen
Venue: WSDM
Year: 2018

Networks are ubiquitous in many high impact domains. Among the various aspects of network studies, connectivity is the one that plays important role in many applications (e.g., information dissemination, robustness analysis, community detection, etc.). The diversified applications have spurred numerous connectivity measures. Accordingly, ad-hoc connectivity optimization methods are designed for each measure, making it hard to model and control the connectivity of the network in a uniformed framework. On the other hand, it is often impossible to maintain an accurate structure of the network due to network dynamics and noise in real applications, which would affect the accuracy of connectivity measures and the effectiveness of corresponding connectivity optimization methods. In this work, we aim to address the challenges on network connectivity by (1)unifying a wide range of classic network connectivity measures into one uniform model; (2)proposing effective approaches to infer connectivity measures and network structures from dynamic and incomplete input data, and (3) providing a general framework to optimize the connectivity measures in the network.
Sequential Recommendation with User Memory Networks
Authors: Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, Hongyuan Zha
Venue: WSDM
Year: 2018

User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user»s historical records and future interests. In this paper, we aim to express, store, and manipulate users» historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users» historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users» sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users» future actions are affected by previous behaviors.
Improving Negative Sampling for Word Representation using Self-embedded Features
Authors: Long Chen, Fajie Yuan, Joemon M. Jose, Weinan Zhang
Venue: WSDM
Year: 2018

Although the word-popularity based negative sampler has shown superb performance in the skip-gram model, the theoretical motivation behind oversampling popular (non-observed) words as negative samples is still not well understood. In this paper, we start from an investigation of the gradient vanishing issue in the skip-gram model without a proper negative sampler. By performing an insightful analysis from the stochastic gradient descent (SGD) learning perspective, we demonstrate, both theoretically and intuitively, negative samples with larger inner product scores are more informative than those with lower scores for the SGD learner in terms of both convergence rate and accuracy. Understanding this, we propose an alternative sampling algorithm that dynamically selects informative negative samples during each SGD update. More importantly, the proposed sampler accounts for multi-dimensional self-embedded features during the sampling process, which essentially makes it more effective than the original popularity-based (one-dimensional) sampler. Empirical experiments further verify our observations and show that our fine-grained samplers gain significant improvement over the existing ones without increasing computational complexity.
Percolator: Scalable Pattern Discovery in Dynamic Graphs
Authors: Sutanay Choudhury, Sumit Purohit, Peng Lin, Yinghui Wu, Lawrence B. Holder, Khushbu Agarwal
Venue: WSDM
Year: 2018

We demonstrate \perco, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of \perco is to dynamically decide and verify a small fraction of patterns and their instances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a( the feasibility of incremental pattern mining by walking through each component of \perco, b) the efficiency and scalability of \perco over the sheer size of real-world dynamic graphs, and c) how the user-friendly \gui of \perco interacts with users to support keyword-based queries that detect, browse and inspect trending patterns. We demonstrate how \perco effectively supports event and trend analysis in social media streams and research publication, respectively.
VISIR: Visual and Semantic Image Label Refinement
Authors: Sreyasi Nag Chowdhury, Niket Tandon, Hakan Ferhatosmanoglu, Gerhard Weikum
Venue: WSDM
Year: 2018

The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO.
Conversational Semantic Search: Looking Beyond Web Search, Q&A and Dialog Systems
Authors: Paul A. Crook, Alex Marin, Vipul Agarwal, Samantha Anderson, Ohyoung Jang, Aliasgar Lanewala, Karthik Tangirala, Imed Zitouni
Venue: WSDM
Year: 2018

User expectations of web search are changing. They are expecting search engines to answer questions, to be more conversational, and to offer means to complete tasks on their behalf. At the same time, to increase the breadth of tasks that personal digital assistants (PDAs), such as Microsoft»s Cortana or Amazon»s Alexa, are capable of, PDAs need to better utilize information about the world, a significant amount of which is available in the knowledge bases and answers built for search engines. It thus seems likely that the underlying systems that power web search and PDAs will converge. This demonstration presents a system that merges elements of traditional multi-turn dialog systems with web based question answering. This demo focuses on the automatic composition of semantic functional units, Botlets, to generate responses to user»s natural language (NL) queries. We show that such a system can be trained to combine information from search engine answers with PDA tasks to enable new user experiences.