OpenRaiser/Intern-Atlas huggingface dataset:
Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes
Authors: Quyu Kong, Rohit Ram, Marian-Andrei Rizoiu
Venue: WSDM
Year: 2021

Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a disconnect between the practitioners of online social media analysis --- usually social, political and communication scientists --- and the accessibility to tools capable of examining online discussions of users. Here we present evently, a tool for modeling online reshare cascades, and particularly retweet cascades, using self-exciting processes. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs, modeling the temporal dynamics of processed retweet cascades and characterizing online users with a wide range of diffusion measures. This tool is designed for researchers with a wide range of computer expertise, and it includes tutorials and detailed documentation. We illustrate the usage of evently with an end-to-end analysis of online user behavior on a topical dataset relating to COVID-19. We show that, by characterizing users solely based on how their content spreads online, we can disentangle influential users and online bots.
WebTour 2021 Workshop on Web Tourism
Authors: Tsvi Kuflik, Catalin-Mihai Barbu, Amra Delic, Dmitri Goldenberg, Julia Neidhardt, Ludovik Coba, Markus Zanker
Venue: WSDM
Year: 2021

Over the years, the Web has become a premier source of information in almost every area we can think about. When considering tourism, the Web became the primary source of information for travelers. When planning trips, people search for information about destinations, accommodations, attractions, means of transportation, in short, everything related to their future trip. Once done searching they reserve almost everything online. The blessing of the easily accessible information comes with the curse of information overload. This brings Web search techniques and recommendation systems come into play. This is especially true recently with the appearance of COVID-19 and the uncertainty and transformative power it brings to travelling. WebTour 2021 brings together researchers and practitioners working on developing and improving tools and techniques for improving users ability to better find relevant information that matches their needs.
Explainable Recommendation with Comparative Constraints on Product Aspects
Authors: Trung-Hoang Le, Hady W. Lauw
Venue: WSDM
Year: 2021

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item. In particular, we propose to anchor reference items on the previously adopted items in a user's history. Not only do we aim at providing comparative explanations involving such items, but we also formulate comparative constraints involving aspect-level comparisons between the target item and the reference items. The framework allows us to incorporate these constraints and integrate them with recommendation objectives involving both types of subjective and objective aspect-level quality assumptions. Experiments on public datasets of several product categories showcase the efficacies of our methodology as compared to baselines at attaining better recommendation accuracies and intuitive explanations.
FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection
Authors: Jia Li, Shimin Di, Yanyan Shen, Lei Chen
Venue: WSDM
Year: 2021

Anomaly detection in time series is a research area of increasing importance. In order to safeguard the availability and stability of services, large companies need to monitor various time-series data to detect anomalies in real time for troubleshooting, thereby reducing potential economic losses. However, in many practical applications, time-series anomaly detection is still an intractable problem due to the huge amount of data, complex data patterns, and limited computational resources. SPOT is an efficient streaming algorithm for anomaly detection, but it is only sensitive to extreme values in the whole data distribution. In this paper, we propose FluxEV, a fast and effective unsupervised anomaly detection framework. By converting the non-extreme anomalies to extreme values, our framework addresses the limitation of SPOT and achieves a huge improvement in the detection accuracy. Moreover, Method of Moments is adopted to speed up the parameter estimation in the automatic thresholding. Extensive experiments show that FluxEV greatly outperforms the state-of-the-art baselines on two large public datasets while ensuring high efficiency.
Abstractive Opinion Tagging
Authors: Qintong Li, Piji Li, Xinyi Li, Zhaochun Ren, Zhumin Chen, Maarten de Rijke
Venue: WSDM
Year: 2021

In e-commerce, opinion tags refer to a ranked list of tags provided by the e-commerce platform that reflect characteristics of reviews of an item. To assist consumers to quickly grasp a large number of reviews about an item, opinion tags are increasingly being applied by e-commerce platforms. Current mechanisms for generating opinion tags rely on either manual labelling or heuristic methods, which is time-consuming and ineffective. In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews. The abstractive opinion tagging task comes with three main challenges: the noisy nature of reviews; the formal nature of opinion tags vs. the colloquial language usage in reviews; and the need to distinguish between different items with very similar aspects. To address these challenges, we propose an abstractive opinion tagging framework, named AOT-Net, to generate a ranked list of opinion tags given a large number of reviews. First, a sentence-level salience estimation component estimates each review's salience score. Next, a review clustering and ranking component ranks reviews in two steps: first, reviews are grouped into clusters and ranked by cluster size; then, reviews within each cluster are ranked by their distance to the cluster center. Finally, given the ranked reviews, a rank-aware opinion tagging component incorporates an alignment feature and alignment loss to generate a ranked list of opinion tags. To facilitate the study of this task, we create and release a large-scale dataset, called eComTag, crawled from real-world e-commerce websites. Extensive experiments conducted on the eComTag dataset verify the effectiveness of the proposed AOT-Net in terms of various evaluation metrics.
Filter Pruning via Probabilistic Model-based Optimization for Accelerating Deep Convolutional Neural Networks
Authors: Qinghua Li, Cuiping Li, Hong Chen
Venue: WSDM
Year: 2021

Accelerating Deep Convolutional Neural Networks(CNNs) has recently received ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. Previous works utilized "smaller-norm-less-important" criterion to prune filters with smaller lp-norm values by pruning and retraining alternately. However, they ignore the effects of $feedback: most current approaches that prune filters only consider the statistics of the filters (e.g., prune filter with small lp-norm values), without considering the performance of the pruned model as an important feedback signal in the next iteration of filter pruning. To solve the problem of non-feedback, we propose a novel filter pruning method, namely Filter Pruning via Probabilistic Model-based Optimization (FPPMO). FPPMO solves the problem of non-feedback by pruning filters in a probabilistic manner. We introduce a pruning probability for each filter, and pruning is guided by sampling from the pruning probability distribution. An optimization method is proposed to update the pruning probability based on the performance of the pruned model in the pruning process. When applied to two image classification benchmarks, the effectiveness of our FPPMO is validated. Notably, on CIFAR-10, our FPPMO reduces more than 57% FLOPs on ResNet-110 with even 0.08% relative accuracy improvement. Moreover, on ILSVRC-2012, our FPPMO reduces more than 50% FLOPs on ResNet-101 without top-5 accuracy drop. Which proving that our FPPMO outperforms the state-of-the-art filter pruning method.
Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
Authors: Roger Zhe Li, Julián Urbano, Alan Hanjalic
Venue: WSDM
Year: 2021

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users. We propose NAECF, a conceptually simple but effective idea to address this bias. The idea consists of adding an autoencoder (AE) layer when learning user and item representations with text-based Convolutional Neural Networks. The AEs, one for the users and one for the items, serve as adversaries to the process of minimizing the rating prediction error when learning how to recommend. They enforce that the specific unique properties of all users and items are sufficiently well incorporated and preserved in the learned representations. These representations, extracted as the bottlenecks of the corresponding AEs, are expected to be less biased towards mainstream users, and to provide more balanced recommendation utility across all users. Our experimental results confirm these expectations, significantly improving the recommendations for non-mainstream users while maintaining the recommendation quality for mainstream users. Our results emphasize the importance of deploying extensive content-based features, such as online reviews, in order to better represent users and items to maximize the de-biasing effect.
Learning Dynamic Embeddings for Temporal Knowledge Graphs
Authors: Siyuan Liao, Shangsong Liang, Zaiqiao Meng, Qiang Zhang
Venue: WSDM
Year: 2021

Representation learning for temporal knowledge graphs has attracted increasing attention in recent years. In this paper, we study the problem of learning dynamic embeddings for temporal knowledge graphs. We address this problem by proposing a Dynamic Bayesian Knowledge Graphs Embedding model (DBKGE), which is able to dynamically track the semantic representations of entities over time in a joint metric space and make predictions for the future. Unlike other temporal knowledge graph embedding methods, DBKGE is a novel probabilistic representation learning method that aims at inferring dynamic embeddings of entities in a streaming scenario. To obtain high-quality embeddings and model their uncertainty, our DBKGE embeds entities with means and variances of Gaussian distributions. Based on amortized inference, an online inference algorithm is proposed to jointly learn the latent representations of entities and smooth their changes across time. Experiments on Yago and Wiki datasets demonstrate that our proposed algorithm outperforms the state-of-the-art static and temporal knowledge graph embedding models.
Origin-Aware Next Destination Recommendation with Personalized Preference Attention
Authors: Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Rui Tan
Venue: WSDM
Year: 2021

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location. However, recent recommendation works do not satisfy this origin-awareness property, and only consider learning from historical destination locations, without origin information. Thus, the resulting approaches are unable to learn and predict origin-aware recommendations based on the user's current location, leading to sub-optimal performance and poor real-world practicality. Hence, in this work, we study the origin-aware next destination recommendation task. We propose the Spatial-Temporal Origin-Destination Personalized Preference Attention (STOD-PPA) encoder-decoder model to learn origin-origin (OO), destination-destination (DD), and origin-destination (OD) relationships by first encoding both origin and destination sequences with spatial and temporal factors in local and global views, then decoding them through personalized preference attention to predict the next destination. Experimental results on seven real-world user trajectory taxi datasets show that our model significantly outperforms baseline and state-of-the-art methods.
FinSense: An Assistant System for Financial Journalists and Investors
Authors: Yi-Ting Liou, Chung-Chi Chen, Tsun-Hsien Tang, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WSDM
Year: 2021

This paper demonstrates FinSense, a system that improves the working efficiency of financial information processing. Given the draft of a financial news story, FinSense extracts the explicit-mentioned stocks and further infers the implicit stocks, providing insightful information for decision making. We propose a novel graph convolutional network model that performs implicit financial instrument inference toward the in-domain data. In addition, FinSense generates candidate headlines for the draft, reducing a significant amount of time in journalism production. The proposed system also provides assistance to investors to sort out the information in the financial news articles.
Deep Recommender Systems Utilizing Side Information
Authors: Amit Livne
Venue: WSDM
Year: 2021

Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheless, many RS suffer from limitations such as data sparsity and cold-start. Side information (SI) can be integrated into a recommender system to tackle these limitations. In my Ph.D. research, I seek to build on and extend the use of SI for RS. Specifically, I propose new types and representations of SI and develop new methods to integrate SI into RS to boost its performance. This paper presents the conceptual foundation and motivation of my Ph.D. research.
All the Wiser: Fake News Intervention Using User Reading Preferences
Authors: Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku
Venue: WSDM
Year: 2021

To address the increasingly significant issue of fake news, we develop a news reading platform in which we propose an implicit approach to reduce people's belief in fake news. Specifically, we leverage reinforcement learning to learn an intervention module on top of a recommender system (RS) such that the module is activated to replace RS to recommend news toward the verification once users touch the fake news. To examine the effect of the proposed method, we conduct a comprehensive evaluation with 89 human subjects and check the effective rate of change in belief but without their other limitations. Moreover, 84% participants indicate the proposed platform can help them defeat fake news. The demo video is available on YouTube https://youtu.be/wKI6nuXu_SM.
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Authors: Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, Xiang Zhang
Venue: WSDM
Year: 2021

Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along the edges of the given graph. Despite their success, however, the existing GNNs are usually sensitive to the quality of the input graph. Real-world graphs are often noisy and contain task-irrelevant edges, which may lead to suboptimal generalization performance in the learned GNN models. In this paper, we propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of GNNs by learning to drop task-irrelevant edges. PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. To take into consideration the topology of the entire graph, the nuclear norm regularization is applied to impose the low-rank constraint on the resulting sparsified graph for better generalization. PTDNet can be used as a key component in GNN models to improve their performances on various tasks, such as node classification and link prediction. Experimental studies on both synthetic and benchmark datasets show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.
Centrality with Diversity
Authors: Liang Lyu, Brandon Fain, Kamesh Munagala, Kangning Wang
Venue: WSDM
Year: 2021

Graph centrality measures use the structure of a network to quantify central or "important" nodes, with applications in web search, social media analysis, and graphical data mining generally. Traditional centrality measures such as the well known PageRank interpret a directed edge as a vote in favor of the importance of the linked node. We study the case where nodes may belong to diverse communities or interests and investigate centrality measures that can identify nodes that are simultaneously important to many such diverse communities. We propose a family of diverse centrality measures formed as fixed point solutions to a generalized nonlinear eigenvalue problem. Our measure can be efficiently computed on large graphs by iterated best response and we study its normative properties on both random graph models and real-world data. We find that we are consistently and efficiently able to identify the most important diverse nodes of a graph, that is, those that are simultaneously central to multiple communities.
ColorfulFeedback: Enhancing Interest Prediction Performance through Multi-dimensional Labeled Feedback from Users
Authors: Yuki Maeda, Shuji Yamaguchi, Tatsuru Higurashi, Kota Tsubouchi
Venue: WSDM
Year: 2021

Recommendation systems help to predict user demand and improve the quality of services offered. While the performance of a recommendation system depends on the quality and quantity of feedback from users, the two major approaches to feedback sacrifice quality for quantity or vice versa; implicit feedback is more abundant but less reliable, while explicit feedback is more credible but harder to collect. Although a hybrid approach has the potential to combine the strengths of both kinds of feedback, the existing approaches using explicit feedback are not suitable for such a combination. In this study, we design a novel feedback suitable for the hybrid approach and use it improve the performance of a recommendation system. The system enables us to collect more varied and less biased feedback from users. It improves performance without requiring major changes to the inference model. It also provides a unique and rich source of information of the model itself. We demonstrate an application of Colorful Feedback showing how it can improve an existing recommendation model.
Community Connect: A Mock Social Media Platform to Study Online Behavior
Authors: Khyati Mahajan, Sourav Roy Choudhury, Sara Levens, Tiffany Gallicano, Samira Shaikh
Venue: WSDM
Year: 2021

We present Community Connect, a custom social media platform for conducting controlled experiments of human behavior. The key distinguishing factor of Community Connect is the ability to control the visibility of user posts based on the groups they belong to, allowing careful and controlled investigation into how information propagates through a social network. We release this platform as a resource to the broader community, to facilitate research on data collected through controlled experiments on social networks.
β-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers
Authors: Dionysis Manousakas, Cecilia Mascolo
Venue: WSDM
Year: 2021

Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers. Despite the multiple benefits of Bayesian methods (such as uncertainty-aware predictions, incorporation of experts knowledge, and hierarchical modeling), the quality of classic Bayesian inference depends critically on whether observations conform with the assumed data generating model, which is impossible to guarantee in practice. In this work, we propose a variational inference method that, in a principled way, can simultaneously scale to large datasets, and robustify the inferred posterior with respect to the existence of outliers in the observed data. Reformulating Bayes theorem via the β-divergence, we posit a robustified generalized Bayesian posterior as the target of inference. Moreover, relying on the recent formulations of Riemannian coresets for scalable Bayesian inference, we propose a sparse variational approximation of the robustified posterior and an efficient stochastic black-box algorithm to construct it. Overall our method allows releasing cleansed data summaries that can be applied broadly in scenarios involving structured and unstructured data contamination. We illustrate the applicability of our approach in diverse simulated and real datasets, and various statistical models, including Gaussian mean inference, logistic and neural linear regression, demonstrating its superiority to existing Bayesian summarization methods in the presence of outliers.
Fairness-Aware Recommendation in Multi-Sided Platforms
Authors: Masoud Mansoury
Venue: WSDM
Year: 2021

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be important to optimize utilities not just for the end user, but also for other entities such as item sellers or producers who desire a fair representation of their items. Existing solutions either lack the multi-sided nature of fairness in recommendations, or do not properly address various aspects of multi-sided fairness in recommendations. In this thesis, we aim at first investigating the impact of unfair recommendations on the system and how it can negatively affect major entities in the system. Then, we seek to propose a general graph-based solution that works as a post processing approach after recommendation generation to tackle the unfairness of recommendations. We plan to perform extensive experiments to evaluate the effectiveness of the proposed approach.