OpenRaiser/Intern-Atlas huggingface dataset:
CORALS: Who Are My Potential New Customers? Tapping into the Wisdom of Customers' Decisions
Authors: Ruirui Li, Jyun-Yu Jiang, Chelsea J.-T. Ju, Wei Wang
Venue: WSDM
Year: 2019

Identifying and recommending potential new customers for local businesses are crucial to the survival and success of local businesses. A key component to identifying the right customers is to understand the decision-making process of choosing a business over the others. However, modeling this process is an extremely challenging task as a decision is influenced by multiple factors. These factors include but are not limited to an individual's taste or preference, the location accessibility of a business, and the reputation of a business from social media. Most of the recommender systems lack the power to integrate multiple factors together and are hardly extensible to accommodate new incoming factors. In this paper, we introduce a unified framework, CORALS, which considers the personal preferences of different customers, the geographical influence, and the reputation of local businesses in the customer recommendation task. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 state-of-the-art methods using two real-world datasets. The results demonstrate that CORALS outperforms all these baselines by a significant margin in most scenarios. In addition to identifying potential new customers, we also break down the analysis for different types of businesses to evaluate the impact of various factors that may affect customers' decisions. This information, in return, provides a great resource for local businesses to adjust their advertising strategies and business services to attract more prospective customers.
Sampling Methods for Counting Temporal Motifs
Authors: Paul Liu, Austin R. Benson, Moses Charikar
Venue: WSDM
Year: 2019

Pattern counting in graphs is fundamental to several network sci- ence tasks, and there is an abundance of scalable methods for estimating counts of small patterns, often called motifs, in large graphs. However, modern graph datasets now contain richer structure, and incorporating temporal information in particular has become a key part of network analysis. Consequently, temporal motifs, which are generalizations of small subgraph patterns that incorporate temporal ordering on edges, are an emerging part of the network analysis toolbox. However, there are no algorithms for fast estimation of temporal motifs counts; moreover, we show that even counting simple temporal star motifs is NP-complete. Thus, there is a need for fast and approximate algorithms. Here, we present the first frequency estimation algorithms for counting temporal motifs. More specifically, we develop a sampling framework that sits as a layer on top of existing exact counting algorithms and enables fast and accurate memory-efficient estimates of temporal motif counts. Our results show that we can achieve one to two orders of magnitude speedups over existing algorithms with minimal and controllable loss in accuracy on a number of datasets.
Overlapping Community Detection in Static and Dynamic Social Networks
Authors: Renny Márquez, Richard Weber
Venue: WSDM
Year: 2019

Networks can be extracted from a wide range of real systems, such as online social networks, communication networks and biological systems. Detection of cohesive groups in these graphs, primarily based on link information, is the goal of community detection. Community structures emerge when a group of nodes is more likely to be linked to each other than to the rest of the network. The modules found can be disjoint or overlapping. Another relevant feature of networks is the possibility to evolve over time. Furthermore, nodes can have valuable information to improve the community detection process. Hence, in this work we propose to design a soft overlapping community detection method for static and dynamic social networks with node attributes. Preliminary results on a toy network are promising.
Modeling Temporal Evidence from External Collections
Authors: Flávio Martins, João Magalhães, Jamie Callan
Venue: WSDM
Year: 2019

Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.
Bridging Models for Popularity Prediction on Social Media
Authors: Swapnil Mishra
Venue: WSDM
Year: 2019

Understanding and predicting the popularity of online items is an important open problem in social media analysis. Most of the recent work on popularity prediction is either based on learning a variety of features from full network data or using generative processes to model the event time data. We identify two gaps in the current state of the art prediction models. The first is the unexplored connection and comparison between the two aforementioned approaches. In our work, we bridge gap between feature-driven and generative models by modelling social cascade with a marked Hawkes self-exciting point process. We then learn a predictive layer on top for popularity prediction using a collection of cascade history. Secondly, the existing methods typically focus on a single source of external influence, whereas for many types of online content such as YouTube videos or news articles, attention is driven by multiple heterogeneous sources simultaneously - e.g. microblogs or traditional media coverage. We propose a recurrent neural network based model for asynchronous streams that connects multiple streams of different granularity via joint inference. We further design two new measures, one to explain the viral potential of videos, the other to uncover latent influences including seasonal trends. This work provides accurate and explainable popularity predictions, as well as computational tools for content producers and marketers to allocate resources for promotion campaigns.
VisCrime: A Crime Visualisation System for Crime Trajectory from Multi-Dimensional Sources
Authors: Ahsan Morshed, Pei-Wei Tsai, Prem Prakash Jayaraman, Timos Sellis, Dimitrios Georgakopoulos, Sam Burke, Shane Joachim, Ming-Sheng Quah, Stefan Tsvetkov, Jason Liew, Corey Jenkins
Venue: WSDM
Year: 2019

Open multidimensional data from existing sources and social media often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, users can more easily interact with and pick out meaningful information from a large dataset. In this paper, we present VisCrime, a system that uses visual analytics to maps out crimes that have occurred in a region/neighbourhood. VisCrime is underpinned by a novel trajectory algorithm that is used to create trajectories from open data sources that reports incidents of crime and data gathered from social media. Our system can be accessed at http://viscrime.ml/deckmap
Lightweight Lexical and Semantic Evidence for Detecting Classes Among Wikipedia Articles
Authors: Marius Pasca, Travis Wolfe
Venue: WSDM
Year: 2019

A supervised method relies on simple, lightweight features in order to distinguish Wikipedia articles that are classes (Shield volcano) from other articles (Kilauea). The features are lexical or semantic in nature. Experimental results in multiple languages over multiple evaluation sets demonstrate the superiority of the proposed method over previous work.
Alexa Everywhere: AI for Daily Convenience
Authors: Rohit Prasad
Venue: WSDM
Year: 2019

The computing industry has been on an inexorable march toward simplifying human-computer interaction, and earlier this decade Amazon bet big on combining voice technology and artificial intelligence. In 2014, with the introduction of Echo and Alexa, Amazon created an entirely new technology category with an AI-first strategy and vision. Since then, Alexa has captured the imagination of customers across the globe, and the company has accelerated the pace of AI research and innovation in support of its promise to improve Alexa every day. In this presentation Rohit Prasad, Vice President and Head Scientist of Amazon Alexa, shares his insights into how recent scientific innovations are advancing Alexa.
Preference Elicitation Strategy for Conversational Recommender System
Authors: Bilih Priyogi
Venue: WSDM
Year: 2019

Traditionally, recommenders have been based on a single-shot model based on past user actions. Conversational recommenders allow incremental elicitation of user preference by performing user-system dialogue. For example, the systems can ask about user preference toward a feature associated with the items. In such systems, it is important to design an efficient conversation, which minimizes the number of question asked while maximizing the preference information obtained. Therefore, this research is intended to explore possible ways to design a conversational recommender with an efficient preference elicitation. Specifically, it focuses on the order of questions. Also, an idea proposed to suggest answers for each question asked, which can assist users in giving their feedback.
Discovering Correlations between Sparse Features in Distant Supervision for Relation Extraction
Authors: Jianfeng Qu, Dantong Ouyang, Wen Hua, Yuxin Ye, Xiaofang Zhou
Venue: WSDM
Year: 2019

The recent art in relation extraction is distant supervision which generates training data by heuristically aligning a knowledge base with free texts and thus avoids human labelling. However, the concerned relation mentions often use the bag-of-words representation, which ignores inner correlations between features located in different dimensions and makes relation extraction less effective. To capture the complex characteristics of relation expression and tighten the correlated features, we attempt to discover and utilise informative correlations between features by the following four phases: 1) formulating semantic similarities between lexical features using the embedding method; 2) constructing generative relation for lexical features with different sizes of side windows; 3) computing correlation scores between syntactic features through a kernel-based method; and 4) conducting a distillation process for the obtained correlated feature pairs and integrating informative pairs with existing relation extraction models. The extensive experiments demonstrate that our method can effectively discover correlation information and improve the performance of state-of-the-art relation extraction methods.
Investment Recommendation System for Low-Liquidity Online Peer to Peer Lending (P2PL) Marketplaces
Authors: Ke Ren, Avinash Malik
Venue: WSDM
Year: 2019

Online P2PL systems allow lending and borrowing between peers without the need for intermediaries such as banks. Convenience and high rate of returns have made P2PL systems very popular. Recommendation systems have been developed to help lenders make wise investment decisions, lowering the chances of overall default. However, P2PL marketplace suffers from low financial liquidity, i.e., loans of different grades are not always available for investment. Moreover, P2PL investments are long term (usually a few years), hence, incorrect investment cannot be liquidated easily. Overall, the state-of-the-art recommendation systems do not account for the low market liquidity and hence, can lead to unwise investment decisions. In this paper we remedy this shortcoming by building a recommendation framework that builds an investment portfolio, which results in the highest return and the lowest risk along with a statistical measure of the number of days required for the amount to be completely funded. Our recommendation system predicts the grade and number of loans that will appear in the future when constructing the investment portfolio. Experimental results show that our recommendation engine outperforms the current state-of-the-art techniques. Our recommendation system can increase the probability of achieving the highest return with the lowest risk by ~ 69%.
Reinforcement Learning to Rank
Authors: Maarten de Rijke
Venue: WSDM
Year: 2019

Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent. Standard machine learning has helped us a lot in the single-turn paradigm, where we use it to predict: intent, relevance, user satisfaction, etc. When we think of search or recommendation as a series of exchanges, we need to turn to bandit algorithms to determine which action the system should take next, or to reinforcement learning to determine not just the next action but also to plan future actions and estimate their potential pay-off. The use of reinforcement learning for search and recommendations comes with a number of challenges, because of the very large action spaces, the large number of potential contexts, and noisy feedback signals characteristic for this domain. This presentation will survey some recent success stories of reinforcement learning for search, recommendation, and conversations; and will identify promising future research directions for reinforcement learning for search and recommendation.
Sequential Variational Autoencoders for Collaborative Filtering
Authors: Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, Vikram Pudi
Venue: WSDM
Year: 2019

Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. In this work, we propose a model which extends variational autoencoders by exploiting the rich information present in the past preference history. We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network. At each time-step of the RNN, the sequence is fed through a series of fully-connected layers, the output of which models the probability distribution of the most likely future preferences. We show that handling temporal information is crucial for improving the accuracy of the VAE: In fact, our model beats the current state-of-the-art by valuable margins because of its ability to capture temporal dependencies among the user-consumption sequence using the recurrent encoder still keeping the fundamentals of variational autoencoders intact.
IDM-WSDM 2019: Workshop on Interactive Data Mining
Authors: Alan Said, Denis Parra, Juhee Bae, Sepideh Pashami
Venue: WSDM
Year: 2019

The first workshop on Interactive Data Mining is held in Melbourne, Australia, on February 15, 2019 and is co-located with 12th ACM International Conference on Web Search and Data Mining (WSDM 2019). The goal of this workshop is to share and discuss research and projects that focus on interaction with and interactivity of data mining systems. The program includes invited speaker, presentation of research papers, and a discussion session.
Conducting Laboratory Experiments Properly with Statistical Tools: An Easy Hands-On Tutorial
Authors: Tetsuya Sakai
Venue: WSDM
Year: 2019

This hands-on half-day tutorial consists of two sessions. Part~I covers the following topics: Preliminaries; Paired and two-sample t-tests, confidence intervals; One-way ANOVA and two-way ANOVA without replication; Familiwise error rate. Part~II covers the following topics: Tukey's HSD test, simultaneous confidence intervals; Randomisation test and randomised Tukey HSD test; What's wrong with statistical significance tests?; Effect sizes, statistical power; Topic set size design and power analysis; Summary: how to report your results. Participants should have some prior knowledge about the very basics of statistical significance testing and are strongly encouraged to bring a laptop with R already installed. They will learn how to design and conduct statistical significance tests for comparing the mean effectiveness scores of two or more systems appropriately, and to report on the test results in an informative manner.
Characterizing and Predicting Email Deferral Behavior
Authors: Bahareh Sarrafzadeh, Ahmed Hassan Awadallah, Christopher H. Lin, Chia-Jung Lee, Milad Shokouhi, Susan T. Dumais
Venue: WSDM
Year: 2019

Email triage involves going throughunhandled emails and deciding what to do with them. This familiar process can become increasingly challenging as the number of unhandled email grows. During a triage session, users commonlydefer handling emails that they cannot immediately deal with to later. These deferred emails, are often related to tasks that are postponed until the user has more time or the right information to deal with them. In this paper, through qualitative interviews and a large-scale log analysis, we study when and whatenterprise email users tend to defer. We found that users are more likely to defer emails when handling them involves replying, reading carefully, or clicking on links and attachments. We also learned that the decision to defer emails depends on many factors such as user's workload and the importance of the sender. Our qualitative results suggested that deferring is very common, and our quantitative log analysis confirms that 12% of triage sessions and 16% of daily active users had at least one deferred email on weekdays. We also discuss severaldeferral strategies such as marking emails as unread and flagging that are reported by our interviewees, and illustrate how such patterns can be also observed in user logs. Inspired by the characteristics of deferred emails and contextual factors involved in deciding if an email should be deferred, we train a classifier for predicting whether a recently triaged email is actually deferred. Our experimental results suggests that deferral can be classified with modest effectiveness. Overall, our work provides novel insights about how users handle their emails and how deferral can be modeled.
Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory
Authors: Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
Venue: WSDM
Year: 2019

Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that this algorithmic focus is myopic. In particular, since learning algorithms generally improve with more and better data, we propose shaping the feedback generation process as an alternate and complementary route to improving accuracy. To this effect, we explore how changes to the user interface can impact the quality and quantity of feedback data -- and therefore the learning accuracy. Motivated by information foraging theory, we study how feedback quality and quantity are influenced by interface design choices along two axes: information scent and information access cost. We present a user study of these interface factors for the common task of picking a movie to watch, showing that these factors can effectively shape and improve the implicit feedback data that is generated while maintaining the user experience.
Visualizing Graph Differences from Social Media Streams
Authors: Minjeong Shin, Dongwoo Kim, Jae Hee Lee, Umanga Bista, Lexing Xie
Venue: WSDM
Year: 2019

We propose KGdiff, a new interactive visualization tool for social media content focusing on entities and relationships. The core component is a layout algorithm that highlights the differences between two graphs. We apply this algorithm on knowledge graphs consisting of named entities and their relations extracted from text streams over different time periods. The visualization system provides additional information such as the volume and frequency ranking of entities and allows users to select which parts of the graph to visualize interactively. On Twitter and news article collections, KGdiff allows users to compare different data subsets. Results of such comparisons often reveal topical or geographical changes in a discussion. More broadly, graph differences are useful for a wide range of relational data comparison tasks, such as comparing social interaction graphs, identifying changes in user behavior, or discovering differences in graphs from distinct sources, geography, or political stance.
Session-Based Social Recommendation via Dynamic Graph Attention Networks
Authors: Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang
Venue: WSDM
Year: 2019

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.
TopExNet: Entity-Centric Network Topic Exploration in News Streams
Authors: Andreas Spitz, Satya Almasian, Michael Gertz
Venue: WSDM
Year: 2019

The recent introduction of entity-centric implicit network representations of unstructured text offers novel ways for exploring entity relations in document collections and streams efficiently and interactively. Here, we present TopExNet as a tool for exploring entity-centric network topics in streams of news articles. The application is available as a web service at https://topexnet.ifi.uni-heidelberg.de.