OpenRaiser/Intern-Atlas huggingface dataset:
Improving website hyperlink structure using server logs
Authors: Ashwin Paranjape, Robert West, Leila Zia, Jure Leskovec
Venue: Conference WSDM 2016
Year: 2016

Good websites should be easy to navigate via hyperlinks, yet maintaining a high-quality link structure is difficult. Identifying pairs of pages that should be linked may be hard for human editors, especially if the site is large and changes frequently. Further, given a set of useful link candidates, the task of incorporating them into the site can be expensive, since it typically involves humans editing pages. In the light of these challenges, it is desirable to develop data-driven methods for automating the link placement task. Here we develop an approach for automatically finding useful hyperlinks to add to a website. We show that passively collected server logs, beyond telling us which existing links are useful, also contain implicit signals indicating which nonexistent links would be useful if they were to be introduced. We leverage these signals to model the future usefulness of yet nonexistent links. Based on our model, we define the problem of link placement under budget constraints and propose an efficient algorithm for solving it. We demonstrate the effectiveness of our approach by evaluating it on Wikipedia, a large website for which we have access to both server logs (used for finding useful new links) and the complete revision history (containing a ground truth of new links). As our method is based exclusively on standard server logs, it may also be applied to any other website, as we show with the example of the biomedical research site Simtk.
Effective named entity recognition for idiosyncratic web collections
Authors: Roman Prokofyev, Gianluca Demartini, Philippe Cudré-Mauroux
Venue: Conference WWW 2014
Year: 2014

Named Entity Recognition (NER) plays an important role in a variety of online information management tasks including text categorization, document clustering, and faceted search. While recent NER systems can achieve near-human performance on certain documents like news articles, they still remain highly domain-specific and thus cannot effectively identify entities such as original technical concepts in scientific documents. In this work, we propose novel approaches for NER on distinctive document collections (such as scientific articles) based on n-grams inspection and classification. We design and evaluate several entity recognition features---ranging from well-known part-of-speech tags to n-gram co-location statistics and decision trees---to classify candidates. In addition, we show how the use of external knowledge bases (either specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic collections. We evaluate our system on two test collections created from a set of Computer Science and Physics papers and compare it against state-of-the-art supervised methods. Experimental results show that a careful combination of the features we propose yield up to 85% NER accuracy over scientific collections and substantially outperforms state-of-the-art approaches such as those based on maximum entropy.
User profiling through deep multimodal fusion
Authors: Golnoosh Farnadi, Jie Tang, Martine De Cock, Marie-Francine Moens
Venue: Conference WSDM 2018
Year: 2018

User profiling in social media has gained a lot of attention due to its varied set of applications in advertising, marketing, recruiting, and law enforcement. Among the various techniques for user modeling, there is fairly limited work on how to merge multiple sources or modalities of user data - such as text, images, and relations - to arrive at more accurate user profiles. In this paper, we propose a deep learning approach that extracts and fuses information across different modalities. Our hybrid user profiling framework utilizes a shared representation between modalities to integrate three sources of data at the feature level, and combines the decision of separate networks that operate on each combination of data sources at the decision level. Our experimental results on more than 5K Facebook users demonstrate that our approach outperforms competing approaches for inferring age, gender and personality traits of social media users. We get highly accurate results with AUC values of more than 0.9 for the task of age prediction and 0.95 for the task of gender prediction.
This is your Twitter on drugs: Any questions?
Authors: Cody Buntain, Jennifer Golbeck
Venue: Conference WWW 2015
Year: 2015

Twitter can be a rich source of information when one wants to monitor trends related to a given topic. In this paper, we look at how tweets can augment a public health program that studies emerging patterns of illicit drug use. We describe the architecture necessary to collect vast numbers of tweets over time based on a large number of search terms and the challenges that come with finding relevant information in the collected tweets. We then show several examples of early analysis we have done on this data, examining temporal and geospatial trends.
Controversy Detection in Wikipedia Using Collective Classification
Authors: Shiri Dori-Hacohen, David D. Jensen, James Allan
Venue: Conference SIGIR 2016
Year: 2016

Concerns over personalization in IR have sparked an interest in detection and analysis of controversial topics. Accurate detection would enable many beneficial applications, such as alerting search users to controversy. Wikipedia's broad coverage and rich metadata offer a valuable resource for this problem. We hypothesize that intensities of controversy among related pages are not independent; thus, we propose a stacked model which exploits the dependencies among related pages. Our approach improves classification of controversial web pages when compared to a model that examines each page in isolation, demonstrating that controversial topics exhibit homophily. Using notions of similarity to construct a subnetwork for collective classification, rather than using the default network present in the relational data, leads to improved classification with wider applications for semi-structured datasets, with the effects most pronounced when a small set of neighbors is used.
Examining the Roles of Automation, Crowds and Professionals Towards Sustainable Fact-Checking
Authors: Naeemul Hassan, Mohammad Yousuf, Md Mahfuzul Haque, Javier A. Suarez Rivas, Md Khadimul Islam
Venue: Conference WWW 2019
Year: 2019

This study explores an online fact-checking community called politicalfactchecking on reddit.com that relies on crowdsourcing to find and verify check-worthy facts relating to U.S. politics. The community embodies a network journalism model in which the process of finding and verifying check-worthy facts through crowdsourcing is coordinated by a team of moderators. Applying the concepts of connective journalism, this study analyzed the posts (N = 543) and comments (N = 10, 221) on the community’s Reddit page to understand differences in roles of the community members and the moderators. A mixed-method approach was used to analyze the data. The authors also developed an automated argument classification model to analyze the contents and identify ways to automate parts of the process. The findings suggest that a model consisting of crowds, professionals, and computer-assisted analysis could increase efficiency and decrease costs in news organizations that involve fact-checking.
ARGH! Automated Rumor Generation Hub
Authors: Larry Huynh, Thai Nguyen, Joshua Goh, Hyoungshick Kim, Jin B. Hong
Venue: Conference CIKM 2021
Year: 2021

It is still challenging to effectively identify rumors due to rapid changes in people's interests and perceptions. To enhance rumor detectors, we first need to better understand which rumors are effective (in terms of bypassing detection) and their characteristics. In this paper, we introduce ARGH, a novel framework to automatically generate rumors using recent advancements in natural language processing, customized to target and generate specific topics. To show the effectiveness of ARGH, we conducted a user study with 212 participants and analyzed how well humans can detect the rumors generated by ARGH, and we also tested its performance against the state-of-the-art rumor detection model PLAN [17]. Surprisingly, the experimental results demonstrate that the generated rumors are significantly harder to identify as rumors than hand-written rumors, degrading the detection accuracy by both humans and machines by 18.87% and 17.62%, respectively. We believe that ARGH will be a useful tool to obtain high quality and evasive rumor datasets quickly, which is often a tedious and time consuming task. Further, our analysis results provide valuable insight into how to characterize evasive rumors and how they can be generated, which will help to enhance the existing rumor detection techniques.
Ensembles of Recurrent Networks for Classifying the Relationship of Fake News Titles
Authors: Ting Su, Craig Macdonald, Iadh Ounis
Venue: Conference SIGIR 2019
Year: 2019

Nowadays, everyone can create and publish news and information anonymously online. However, the credibility of such news and information are not guaranteed. To differentiate fake news from genuine news, one can compare a recent news with earlier posted ones. Identified suspicious news can be debunked to stop the fake news from spreading further. In this paper, we investigate the advantages of recurrent neural networks-based language representations (e.g., BERT, BiLSTM) in order to build ensemble classifiers that can accurately predict if one news title is related to, and, additionally disagrees with an earlier news title. Our experiments, on a dataset of 321k news titles created for the WSDM 2019 challenge, show that the BERT-based models significantly outperform BiLSTM, which in-turn significantly outperforms a simpler embedding-based representation. Furthermore, even the state-of-the-art BERT approach can be enhanced when combined with a simple BM25 feature.
Learning from Cross-Modal Behavior Dynamics with Graph- Regularized Neural Contextual Bandit
Authors: Xian Wu, Suleyman Cetintas, Deguang Kong, Miao Lu, Jian Yang, Nitesh V. Chawla
Venue: Conference WWW 2020
Year: 2020

Contextual multi-armed bandit algorithms have received significant attention in modeling users’ preferences for online personalized recommender systems in a timely manner. While significant progress has been made along this direction, a few major challenges have not been well addressed yet: (i) a vast majority of the literature is based on linear models that cannot capture complex non-linear inter-dependencies of user-item interactions; (ii) existing literature mainly ignores the latent relations among users and non-recommended items: hence may not properly reflect users’ preferences in the real-world; (iii) current solutions are mainly based on historical data and are prone to cold-start problems for new users who have no interaction history. To address the above challenges, we develop a Graph Regularized Cross-modal (GRC) learning model, a general framework to exploit transferable knowledge learned from user-item interactions as well as the external features of users and items in online personalized recommendations. In particular, the GRC framework leverage a non-linearity of neural network to model complex inherent structure of user-item interactions. We further augment GRC with the cooperation of the metric learning technique and a graph-constrained embedding module, to map the units from different dimensions (temporal, social and semantic) into the same latent space. An extensive set of experiments are conducted on two benchmark datasets as well as a large scale proprietary dataset from a major search engine demonstrates the power of the proposed GRC model in effectively capturing users’ dynamic preferences under different settings by outperforming all baselines by a large margin.
Guide focused crawler efficiently and effectively using on-line topical importance estimation
Authors: Ziyu Guan, Can Wang, Chun Chen, Jiajun Bu, Junfeng Wang
Venue: Conference SIGIR 2008
Year: 2008

Focused crawling is a critical technique for topical resource discovery on the Web. We propose a new frontier prioritizing algorithm, namely, the OTIE (On-line Topical Importance Estimation) algorithm, which efficiently and effectively combines link-based and content-based analysis to evaluate the priority of an uncrawled URL in the frontier. We then demonstrate OTIE's advantages over traditional prioritizing algorithms by real crawling experiments.
Focused Crawling for Structured Data
Authors: Robert Meusel, Peter Mika, Roi Blanco
Venue: Conference CIKM 2014
Year: 2014

The Web is rapidly transforming from a pure document collection to the largest connected public data space. Semantic annotations of web pages make it notably easier to extract and reuse data and are increasingly used by both search engines and social media sites to provide better search experiences through rich snippets, faceted search, task completion, etc. In our work, we study the novel problem of crawling structured data embedded inside HTML pages. We describe Anthelion, the first focused crawler addressing this task. We propose new methods of focused crawling specifically designed for collecting data-rich pages with greater efficiency. In particular, we propose a novel combination of online learning and bandit-based explore/exploit approaches to predict data-rich web pages based on the context of the page as well as using feedback from the extraction of metadata from previously seen pages. We show that these techniques significantly outperform state-of-the-art approaches for focused crawling, measured as the ratio of relevant pages and non-relevant pages collected within a given budget.
Enumerating fair packages for group recommendations
Authors: Ryoma Sato
Venue: Conference WSDM 2022
Year: 2022

Package-to-group recommender systems recommend a set of unified items to a group of people. Different from conventional settings, it is not easy to measure the utility of group recommendations because it involves more than one user. In particular, fairness is crucial in group recommendations. Even if some members in a group are substantially satisfied with a recommendation, it is undesirable if other members are ignored to increase the total utility. Many methods for evaluating and applying the fairness of group recommendations have been proposed in the literature. However, all these methods maximize the score and output only one package. This is in contrast to conventional recommender systems, which output several (e.g., top-K) candidates. This can be problematic because a group can be dissatisfied with the recommended package owing to some unobserved reasons, even if the score is high. To address this issue, we propose a method to enumerate fair packages efficiently. Our method furthermore supports filtering queries, such as top-K and intersection, to select favorite packages when the list is long. We confirm that our algorithm scales to large datasets and can balance several aspects of the utility of the packages.
Retrieving black-box optimal images from external databases
Authors: Ryoma Sato
Venue: Conference WSDM 2022
Year: 2022

Suppose we have a black-box function (e.g., deep neural network) that takes an image as input and outputs a value that indicates preference. How can we retrieve optimal images with respect to this function from an external database on the Internet? Standard retrieval problems in the literature (e.g., item recommendations) assume that an algorithm has full access to the set of items. In other words, such algorithms are designed for service providers. In this paper, we consider the retrieval problem under different assumptions. Specifically, we consider how users with limited access to an image database can retrieve images using their own black-box functions. This formulation enables a flexible and finer-grained image search defined by each user. We assume the user can access the database through a search query with tight API limits. Therefore, a user needs to efficiently retrieve optimal images in terms of the number of queries. We propose an efficient retrieval algorithm Tiara for this problem. In the experiments, we confirm that our proposed method performs better than several baselines under various settings.
Mining Cross Features for Financial Credit Risk Assessment
Authors: Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen, Jun Zhu
Venue: Conference CIKM 2021
Year: 2021

For reliability, machine learning models in some areas, e.g., finance and healthcare, require to be both accurate and globally interpretable. Among them, credit risk assessment is a major application of machine learning for financial institutions to evaluate credit of users and detect default or fraud. Simple white-box models, such as Logistic Regression (LR), are usually used for credit risk assessment, but not powerful enough to model complex nonlinear interactions among features. In contrast, complex black-box models are powerful at modeling, but lack of interpretability, especially global interpretability. Fortunately, automatic feature crossing is a promising way to find cross features to make simple classifiers to be more accurate without heavy handcrafted feature engineering. However, existing automatic feature crossing methods have problems in efficiency on credit risk assessment, for corresponding data usually contains hundreds of feature fields. In this work, we find local interpretations in Deep Neural Networks (DNNs) of a specific feature are usually inconsistent among different samples. We demonstrate this is caused by nonlinear feature interactions in the hidden layers of DNN. Thus, we can mine feature interactions in DNN, and use them as cross features in LR. This will result in mining cross features more efficiently. Accordingly, we propose a novel automatic feature crossing method called DNN2LR. The final model, which is a LR model empowered with cross features, generated by DNN2LR is a white-box model. We conduct experiments on both public and business datasets from real-world credit risk assessment applications, which show that, DNN2LR outperform both conventional models used for credit assessment and several feature crossing methods. Moreover, comparing with state-of-the-art feature crossing methods, i.e., AutoCross, the proposed DNN2LR method accelerates the speed by about 10 to 40 times on financial credit assessment datasets, which contain hundreds of feature fields.
A Content Management Perspective on Fact-Checking
Authors: Sylvie Cazalens, Philippe Lamarre, Julien Leblay, Ioana Manolescu, Xavier Tannier
Venue: Conference WWW 2018
Year: 2018

Fact checking has captured the attention of the media and the public alike; it has also recently received strong attention from the computer science community, in particular from data and knowledge management, natural language processing and information retrieval; we denote these together under the term "content management". In this paper, we identify the fact checking tasks which can be performed with the help of content management technologies, and survey the recent research works in this area, before laying out some perspectives for the future. We hope our work will provide interested researchers, journalists and fact checkers with an entry point in the existing literature as well as help develop a roadmap for future research and development work.
Mining the stars: learning quality ratings with user-facing explanations for vacation rentals
Authors: Anastasiia Kornilova, Lucas Bernardi
Venue: Conference WSDM 2021
Year: 2021

Online Travel Platforms are virtual two-sided marketplaces where guests search for accommodations and accommodation providers list their properties such as hotels and vacation rentals. The large majority of hotels are rated by official institutions with a number of stars indicating the quality of service they provide. It is a simple and effective mechanism that contributes to match supply with demand by helping guests to find options meeting their criteria and accommodation suppliers to market their product to the right segment directly impacting the number of transactions on the platform. Unfortunately, no similar rating system exists for the large majority of vacation rentals, making it difficult for guests to search and compare options and hard for vacation rentals suppliers to market their product effectively. In this work we describe a machine learned quality rating system for vacation rentals. The problem is challenging, mainly due to explainability requirements and the lack of ground truth. We present techniques to address these challenges and empirical evidence of their efficacy. Our system was successfully deployed and validated through Online Controlled Experiments performed in Booking. com, a large Online Travel Platform, and running for more than one year, impacting more than a million accommodations and millions of guests.
Monetary discount strategies for real-time promotion campaign
Authors: Ying-Chun Lin, Chi-Hsuan Huang, Chu-Cheng Hsieh, Yu-Chen Shu, Kun-Ta Chuang
Venue: Conference WWW 2017
Year: 2017

The effectiveness of monetary promotions has been well reported in the literature to affect shopping decisions for products in real life experience. Nowadays, e-commerce retailers are facing more fierce competition on price promotion in that consumers can easily use a search engine to find another merchant selling an identical product for comparing price.
Overview of the Supporting and Understanding of Conversational Dialogues (SUD) Workshop
Authors: Debasis Ganguly, Manisha Verma, Procheta Sen, Dipasree Pal, Gareth J. F. Jones
Venue: Conference WSDM 2021
Year: 2021

The workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) seeks to encourage researchers to investigate automated methods to analyze and understand conversations, and also explore methodologies for proactively providing assistance to the communicating parties during conversations, ranging from summarizing the minutes of meetings to automatically keeping track of action items etc. The workshop will have (1) a regular research paper track, and a more focused (2) data challenge track, inviting papers on a specific task of contextualizing entities of interest from conversation dialogues.
Towards motivational and empathetic response generation in online mental health support
Authors: Tulika Saha, Vaibhav Gakhreja, Anindya Sundar Das, Souhitya Chakraborty, Sriparna Saha
Venue: Conference SIGIR 2022
Year: 2022

The scarcity of Mental Health Professionals (MHPs) available to assist patients underlines the need for developing automated systems to help MHPs combat the grievous mental illness called Major Depressive Disorder. In this paper, we develop a Virtual Assistant (VA) that serves as a first point of contact for users who are depressed or disheartened. In support based conversations, two primary components have been identified to produce positive outcomes,empathy andmotivation. While empathy necessitates acknowledging the feelings of the users with a desire to help, imparting hope and motivation uplifts the spirit of support seekers in distress. A combination of these aspects will ensure generalized positive outcome and beneficial alliance in mental health support. The VA, thus, should be capable of generating empathetic and motivational responses, continuously demonstrating positive sentiment by the VA. The end-to-end system employs two mechanisms in a pipe-lined manner : (i)Motivational Response Generator (MRG) : a sentiment driven Reinforcement Learning (RL) based motivational response generator; and (ii)Empathetic Rewriting Framework (ERF) : a transformer based model that rewrites the response from MRG to induce empathy. Experimental results indicate that our proposed VA outperforms several of its counterparts. To the best of our knowledge, this is the first work that seeks to incorporate these aspects together in an end-to-end system.