OpenRaiser/Intern-Atlas huggingface dataset:
Harnessing the Power of Data Science through Research
Authors: Andrew Blake
Venue: WSDM
Year: 2017

The Alan Turing Institute is the UK's newly-created national centre for data science, headquartered at the British Library in the heart of London's vibrant Knowledge Quarter. Our vision is to become a world leader in data science research and innovation. This lecture will take a whistle-stop tour through the Institute's scientific and innovation programme. It tackles challenges of social and economic importance, from engineering to finance, from health and well-being to cloud computing and smart cities. The Institute aims to make unique contributions, focussing on work that is complementary to what can be done in universities. In particular, we aim to act as a national hub, attracting a diversity of talent, interest and influence. We emphasise engineering and the ability to build high quality prototypes. We encompass, in one connected physical space, a sweep of disciplines from pure mathematics, through statistics and machine learning, to social science.
Click Through Rate Prediction for Local Search Results
Authors: Fidel Cacheda, Nicola Barbieri, Roi Blanco
Venue: WSDM
Year: 2017

With the ubiquity of internet access and location services provided by smartphone devices, the volume of queries issued by users to find products and services that are located near them is rapidly increasing. Local search engines help users in this task by matching queries with a predefined geographical connotation ("local queries") against a database of local business listings.
Real-Time Bidding by Reinforcement Learning in Display Advertising
Authors: Han Cai, Kan Ren, Weinan Zhang, Kleanthis Malialis, Jun Wang, Yong Yu, Defeng Guo
Venue: WSDM
Year: 2017

The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks. The empirical study on two large-scale real-world datasets and the live A/B testing on a commercial platform have demonstrated the superior performance and high efficiency compared to state-of-the-art methods.
Keeping Apace with Progress in Natural Language Processing
Authors: Claire Cardie
Venue: WSDM
Year: 2017

Increasingly central to online search and information discovery are methods from Natural Language Processing (NLP). Research in the field, however, is progressing at what feels a frenetic pace, making it a daunting task to keep up with the latest results.
Social Incentive Optimization in Online Social Networks
Authors: Guangde Chen, Bee-Chung Chen, Deepak Agarwal
Venue: WSDM
Year: 2017

Most online social networks provide a mechanism for users to broadcast messages to their personalized network through actions like shares, likes and tweets. Receiving positive feedback from the network such as likes, comments and retweets in response to such actions can provide a strong incentive for users to broadcast more often in the future. We call such feedback by the network, that influences a user to perform certain desirable future actions, social incentives. For example, after a user shares an article to her social network, receiving positive feedback such as a ''like'' from a friend can potentially encourage her to continue sharing more regularly. Typically, for every user's visit to an online social network site, good messages need to be ranked and selected by a recommender system from a large set of candidate messages (broadcasted by the user's network). In this paper, we propose a novel recommendation problem: How should we recommend messages to users to incentivize neighbors in their personal network to perform desirable actions in the future with high likelihood, without significantly hurting overall engagement for the entire system? For instance, messages could be content shared by neighbors. The goal in this case would be to encourage more content shares in the future. We call this problem social incentive optimization and study an instance of it for LinkedIn's news feed. We observe that a user who receives positive social feedback from neighbors has a higher likelihood of broadcasting more frequently. Using this observation, we develop a novel recommendation framework that incentivize users to broadcast more often, without significantly hurting overall feed engagement. We demonstrate the effectiveness of our approach through causal analysis on retrospective data and online A/B experiments.
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification
Authors: Ting Chen, Yizhou Sun
Venue: WSDM
Year: 2017

In this paper, we study the problem of author identification under double-blind review setting, which is to identify potential authors given information of an anonymized paper. Different from existing approaches that rely heavily on feature engineering, we propose to use network embedding approach to address the problem, which can automatically represent nodes into lower dimensional feature vectors. However, there are two major limitations in recent studies on network embedding: (1) they are usually general-purpose embedding methods, which are independent of the specific tasks; and (2) most of these approaches can only deal with homogeneous networks, where the heterogeneity of the network is ignored. Hence, challenges faced here are two folds: (1) how to embed the network under the guidance of the author identification task, and (2) how to select the best type of information due to the heterogeneity of the network. To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model. In our model, nodes are first embedded as vectors in latent feature space. Embeddings are then shared and jointly trained according to task-specific and network-general objectives. We extend the existing unsupervised network embedding to incorporate meta paths in heterogeneous networks, and select paths according to the specific task. The guidance from author identification task for network embedding is provided both explicitly in joint training and implicitly during meta path selection. Our experiments demonstrate that by using path-augmented network embedding with task guidance, our model can obtain significantly better accuracy at identifying the true authors comparing to existing methods.
WSDM 2017 Workshop on Mining Online Health Reports: MOHRS 2017
Authors: Nigel Collier, Nut Limsopatham, Aron Culotta, Mike Conway, Ingemar J. Cox, Vasileios Lampos
Venue: WSDM
Year: 2017

The workshop on Mining Online Health Reports (MOHRS) draws upon the rapidly developing field of Computational Health, focusing on textual content that has been generated through the various facets of Web activity. Online user-generated information mining, especially from social media platforms and search engines, has been in the forefront of many research efforts, especially in the fields of Information Retrieval and Natural Language Processing. The incorporation of such data and techniques in a number of health-oriented applications has provided strong evidence about the potential benefits, which include better population coverage, timeliness and the operational ability in places with less established health infrastructure. The workshop aims to create a platform where relevant state-of-the-art research is presented, but at the same time discussions among researchers with cross-disciplinary backgrounds can take place. It will focus on the characterisation of data sources, the essential methods for mining this textual information, as well as potential real-world applications and the arising ethical issues. MOHRS '17 will feature 3 keynote talks and 4 accepted paper presentations, together with a panel discussion session.
Neural Models for Full Text Search
Authors: Nick Craswell
Venue: WSDM
Year: 2017

A fundamental concern in search engines is to determine which documents have the best content for satisfying the user, based on analysis of the user's query and the text of documents. For this query-content match, many learning to rank systems make use of IR features developed in the 1990s in the TREC framework. Such features are still important in a variety of search tasks, and particularly in the long tail where clicks, links and social media signals become sparse. I will present our current progress, in particular three different neural models, with the goal of surpassing the 1990s models in full text search. This will include evidence, using proprietary Bing datasets, that large-scale training data can be useful. I will also argue that for the field to make progress on query-content relevance modeling, it may be valuable to set up a shared blind evaluation similar to 1990s TREC, possibly with large-scale training data.
Recommender Systems: Research Direction
Authors: Manoj Reddy Dareddy
Venue: WSDM
Year: 2017

Recommender systems are omnipresent on the web. They aim to address the problem of information overload. Recommender systems personalizes the content to each individual user based on their preferences. Currently, these systems are used to recommend movies, music, news, products to buy etc. They essentially assist us in making decisions. This paper presents the author's plan for his PhD research in this domain and research questions to be discussed at the WSDM 2017 Doctoral Consortium.
Utilizing Knowledge Graphs in Text-centricInformation Retrieval
Authors: Laura Dietz, Alexander Kotov, Edgar Meij
Venue: WSDM
Year: 2017

The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The increasing depth and breadth of content in KGs makes them not only rich sources of structured knowledge by themselves but also valuable resources for search systems. A surge of recent developments in entity linking and retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications, making this an ideal time to pause and report current findings to the community, summarizing successful approaches, and soliciting new ideas. This tutorial is the first to disseminate the progress in this emerging field to researchers and practitioners.
Modeling Navigation in Information Networks
Authors: Dimitar Dimitrov
Venue: WSDM
Year: 2017

Navigation in an information space is a natural way to explore and discover its content. Information systems on the Web like digital encyclopedias (e.g., Wikipedia) are interested in providing good navigational support to their users. To that end, navigation models can be useful for estimating the general navigability of an information space and for understanding how users interact with it. Such models can also be applied to identify problems faced by the users during navigation and to improve user interfaces.
Mining Actionable Insights from Social Networksat WSDM 2017
Authors: Faezeh Ensan, Zeinab Noorian, Ebrahim Bagheri
Venue: WSDM
Year: 2017

The first international workshop on Mining Actionable Insights from Social Networks (MAISoN'17) is to be held on February 10, 2017; co-located with the Tenth ACM International Web Search and Data Mining (WSDM) Conference in Cambridge, UK. MAISoN'17 aims at bringing together researchers and participants from different disciplines such as computer science, big data mining, machine learning, social network analysis and other related areas in order to identify challenging problems and share ideas, algorithms, and technologies for mining actionable insight from social network data. We organized a workshop program that includes the presentation of eight peer-reviewed papers and keynote talks, which foster discussions around state-of-the-art in social network mining and will hopefully lead to future collaborations and exchanges.
Modeling Air Travel Choice Behavior with Mixed Kernel Density Estimations
Authors: Zhenni Feng, Yanmin Zhu, Jian Cao
Venue: WSDM
Year: 2017

Understanding air travel choice behavior of air passengers is of great significance for various purposes such as travel demand prediction and trip recommendation. Existing approaches based on surveys can only provide aggregate level air travel choice behavior of passengers and they fail to provide comprehensive information for personalized services. In this paper we focus on modeling individual level air travel choice behavior of passengers, which is valuable for recommendations and personalized services. We employ a probabilistic model to represent individual level air travel choice behavior based on a large dataset of historical booking records, leveraging several key factors, such as takeoff time, arrival time, elapsed time between reservation and takeoff, price, and seat class. However, each passenger has only a limited number of historical booking records, causing a serious data sparsity problem. To this end, we propose a mixed kernel density estimation (mix-KDE) approach for each passenger with a mixture model that combines probabilistic estimation of both regularity of the individual himself and social conformity of similar passengers. The proposed model is trained and evaluated via the expectation-maximization (EM) algorithm with a huge dataset of booking records of over 10 million air passengers from a popular online travel agency in China. Experimental results demonstrate that our mix-KDE approach outperforms the Gaussian mixture model (GMM) and the simple kernel density estimation in the presence of the sparsity issue.
Quantifying and Bursting the Online Filter Bubble
Authors: Kiran Garimella
Venue: WSDM
Year: 2017

In this thesis, we develop methods to (i) detect and quantify the existence of filter bubbles in social media, (ii) monitor their evolution over time, and finally, (iii) devise methods to overcome the effects caused by filter bubbles. We are the first to propose an end-to-end system that solves the problem of filter bubbles completely algorithmically. We build on top of existing studies and ideas from social science with principles from graph theory to design algorithms which are language independent, domain agnostic and scalable to large number of users.
Reducing Controversy by Connecting Opposing Views
Authors: Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis
Venue: WSDM
Year: 2017

Society is often polarized by controversial issues that split the population into groups with opposing views. When such issues emerge on social media, we often observe the creation of `echo chambers', i.e., situations where like-minded people reinforce each other's opinion, but do not get exposed to the views of the opposing side. In this paper we study algorithmic techniques for bridging these chambers, and thus reduce controversy. Specifically, we represent the discussion on a controversial issue with an endorsement graph, and cast our problem as an edge-recommendation problem on this graph. The goal of the recommendation is to reduce the controversy score of the graph, which is measured by a recently-developed metric based on random walks. At the same time, we take into account the acceptance probability of the recommended edge, which represents how likely the edge is to materialize in the endorsement graph. We propose a simple model based on a recently-developed user-level controversy score, that is competitive with state-of-the-art link-prediction algorithms. Our goal then becomes finding the edges that produce the largest reduction in the controversy score, in expectation. To solve this problem, we propose an efficient algorithm that considers only a fraction of all the possible combinations of edges. Experimental results show that our algorithm is more efficient than a simple greedy heuristic, while producing comparable score reduction. Finally, a comparison with other state-of-the-art edge-addition algorithms shows that this problem is fundamentally different from what has been studied in the literature.
Not Enough Data?: Joint Inferring Multiple Diffusion Networks via Network Generation Priors
Authors: Xinran He, Yan Liu
Venue: WSDM
Year: 2017

Network Inference, i.e., discovering latent diffusion networks from observed cascades, has been studied extensively in recent years, leading to a series of excellent work. However, it has been observed that the accuracy of existing methods deteriorates significantly when the number of cascades are limited (compared with the large number of nodes), which is the norm in real world applications. Meanwhile, we are able to collect cascades on many different topics or over a long time period: the associated influence networks (either topic-specific or time-specific) are highly correlated while the number of cascade observations associated with each network is very limited. In this work, we propose a generative model, referred to as the MultiCascades model (MCM), to address the challenge of data scarcity by exploring the commonality between multiple related diffusion networks. MCM builds a hierarchical graphical model, where all the diffusion networks share the same network prior, e.g., the popular Stochastic Blockmodels or the latent space models. The parameters of the network priors can be effectively learned by gleaning evidence from a large number of inferred networks. In return, each individual network can be inferred more accurately thanks to the prior information. Furthermore, we develop efficient inference and learning algorithms so that MCM is scalable for practical applications. The results on both synthetic datasets and real-world datasets demonstrate that MCM infers both topic-specific and time-varying diffusion networks more accurately.
Machine Learning at Amazon
Authors: Ralf Herbrich
Venue: WSDM
Year: 2017

In this talk I will give an introduction into the field of machine learning and discuss why it is a crucial technology for Amazon.
Label Informed Attributed Network Embedding
Authors: Xiao Huang, Jundong Li, Xia Hu
Venue: WSDM
Year: 2017

Attributed network embedding aims to seek low-dimensional vector representations for nodes in a network, such that original network topological structure and node attribute proximity can be preserved in the vectors. These learned representations have been demonstrated to be helpful in many learning tasks such as network clustering and link prediction. While existing algorithms follow an unsupervised manner, nodes in many real-world attributed networks are often associated with abundant label information, which is potentially valuable in seeking more effective joint vector representations. In this paper, we investigate how labels can be modeled and incorporated to improve attributed network embedding. This is a challenging task since label information could be noisy and incomplete. In addition, labels are completely distinct with the geometrical structure and node attributes. The bewildering combination of heterogeneous information makes the joint vector representation learning more difficult. To address these issues, we propose a novel Label informed Attributed Network Embedding (LANE) framework. It can smoothly incorporate label information into the attributed network embedding while preserving their correlations. Experiments on real-world datasets demonstrate that the proposed framework achieves significantly better performance compared with the state-of-the-art embedding algorithms.
Unbiased Learning-to-Rank with Biased Feedback
Authors: Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
Venue: WSDM
Year: 2017

Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a Propensity-Weighted Ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. In contrast to most conventional approaches to de-biasing the data using click models, this allows training of ranking functions even in settings where queries do not repeat. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model misspecification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.
iPhone's Digital Marketplace: Characterizing the Big Spenders
Authors: Farshad Kooti, Mihajlo Grbovic, Luca Maria Aiello, Eric Bax, Kristina Lerman
Venue: WSDM
Year: 2017

With mobile shopping surging in popularity, people are spending ever more money on digital purchases through their mobile devices and phones. However, few large-scale studies of mobile shopping exist. In this paper we analyze a large data set consisting of more than 776M digital purchases made on Apple mobile devices that include songs, apps, and in-app purchases. We find that 61% of all the spending is on in-app purchases and that the top 1% of users are responsible for 59% of all the spending. These big spenders are more likely to be male and older, and less likely to be from the US. We study how they adopt and abandon individual app, and find that, after an initial phase of increased daily spending, users gradually lose interest: the delay between their purchases increases and the spending decreases with a sharp drop toward the end. Finally, we model the in-app purchasing behavior in multiple steps: 1) we model the time between purchases; 2) we train a classifier to predict whether the user will make a purchase from a new app or continue purchasing from the existing app; and 3) based on the outcome of the previous step, we attempt to predict the exact app, new or existing, from which the next purchase will come. The results yield new insights into spending habits in the mobile digital marketplace.