OpenRaiser/Intern-Atlas huggingface dataset:
AttriInfer: Inferring user attributes in online social networks using markov random fields
Authors: Jinyuan Jia, Binghui Wang, Le Zhang, Neil Zhenqiang Gong
Venue: Conference WWW 2017
Year: 2017

In the attribute inference problem, we aim to infer users' private attributes (e.g., locations, sexual orientation, and interests) using their public data in online social networks. State-of-the-art methods leverage a user's both public friends and public behaviors (e.g., page likes on Facebook, apps that the user reviewed on Google Play) to infer the user's private attributes. However, these methods suffer from two key limitations: 1) suppose we aim to infer a certain attribute for a target user using a training dataset, they only leverage the labeled users who have the attribute, while ignoring the label information of users who do not have the attribute; 2) they are inefficient because they infer attributes for target users one by one. As a result, they have limited accuracies and applicability in real-world social networks.
Randomization tests for distinguishing social influence and homophily effects
Authors: Timothy La Fond, Jennifer Neville
Venue: Conference WWW 2010
Year: 2010

Relational autocorrelation is ubiquitous in relational domains. This observed correlation between class labels of linked instances in a network (e.g., two friends are more likely to share political beliefs than two randomly selected people) can be due to the effects of two different social processes. If social influence effects are present, instances are likely to change their attributes to conform to their neighbor values. If homophily effects are present, instances are likely to link to other individuals with similar attribute values. Both these effects will result in autocorrelated attribute values. When analyzing static relational networks it is impossible to determine how much of the observed correlation is due each of these factors. However, the recent surge of interest in social networks has increased the availability of dynamic network data. In this paper, we present a randomization technique for temporal network data where the attributes and links change over time. Given data from two time steps, we measure the gain in correlation and assess whether a significant portion of this gain is due to influence and/or homophily. We demonstrate the efficacy of our method on semi-synthetic data and then apply the method to a real-world social networks dataset, showing the impact of both influence and homophily effects.
Detecting anomalies in space using multivariate convolutional LSTM with mixtures of probabilistic PCA
Authors: Shahroz Tariq, Sangyup Lee, Youjin Shin, Myeong Shin Lee, Okchul Jung, Daewon Chung, Simon S. Woo
Venue: Conference KDD 2019
Year: 2019

Detecting an anomaly is not only important for many terrestrial applications on Earth but also for space applications. Especially, satellite missions are highly risky because unexpected hardware and software failures can occur due to sudden or unforeseen space environment changes. Anomaly detection and spacecraft health monitoring systems have heavily relied on human expertise to investigate whether they are a true anomaly or not. Also, it is practically infeasible to produce labels on data due to the enormous amount of telemetries generated from a satellite. In this work, we propose a data-driven anomaly detection algorithm for Korea Multi-Purpose Satellite 2 (KOMPSAT-2). We develop a Multivariate Convolution LSTM with Mixtures of Probabilistic Principal Component Analyzers, where our approach uses both neural networks and probabilistic clustering to improve the anomaly detection performance. We evaluated our approach with a total of 22 million telemetry samples collected for 10 months from KOMPSAT-2. We also compare our approach with other state-of-the-art approaches. We show that our proposed approach is 35.8% better in precision, and 18.2% better in F-1 score than the best baseline approach. We plan to deploy our algorithm in the second half of 2019 to actually apply real operation of KOMPSAT-2.
Understanding and Predicting Graded Search Satisfaction
Authors: Jiepu Jiang, Ahmed Hassan Awadallah, Xiaolin Shi, Ryen W. White
Venue: Conference WSDM 2015
Year: 2015

Understanding and estimating satisfaction with search engines is an important aspect of evaluating retrieval performance. Research to date has modeled and predicted search satisfaction on a binary scale, i.e., the searchers are either satisfied or dissatisfied with their search outcome. However, users' search experience is a complex construct and there are different degrees of satisfaction. As such, binary classification of satisfaction may be limiting. To the best of our knowledge, we are the first to study the problem of understanding and predicting graded (multi-level) search satisfaction. We ex-amine sessions mined from search engine logs, where searcher satisfaction was also assessed on multi-point scale by human annotators. Leveraging these search log data, we observe rich and non-monotonous changes in search behavior in sessions with different degrees of satisfaction. The findings suggest that we should predict finer-grained satisfaction levels. To address this issue, we model search satisfaction using features indicating search outcome, search effort, and changes in both outcome and effort during a session. We show that our approach can predict subtle changes in search satisfaction more accurately than state-of-the-art methods, affording greater insight into search satisfaction. The strong performance of our models has implications for search providers seeking to accu-rately measure satisfaction with their services.
Jointly Leveraging Intent and Interaction Signals to Predict User Satisfaction with Slate Recommendations
Authors: Rishabh Mehrotra, Mounia Lalmas, Doug Kenney, Thomas Lim-Meng, Golli Hashemian
Venue: Conference WWW 2019
Year: 2019

Detecting and understanding implicit measures of user satisfaction are essential for enhancing recommendation quality. When users interact with a recommendation system, they leave behind fine grained traces of interaction signals, which contain valuable information that could help gauging user satisfaction. User interaction with such systems is often motivated by a specific need or intent, often not explicitly specified by the user, but can nevertheless inform on how the user interacts with, and the extent to which the user is satisfied by the recommendations served. In this work, we consider a complex recommendation scenario, called Slate Recommendation, wherein a user is presented with an ordered set of collections, called slates, in a specific page layout. We focus on the context of music streaming and leverage fine-grained user interaction signals to tackle the problem of predicting user satisfaction. We hypothesize that user interactions are conditional on the specific intent users have when interacting with a recommendation system, and highlight the need for explicitly considering user intent when interpreting interaction signals. We present diverse approaches to identify user intents (interviews, surveys and a quantitative approach) and identify a set of common intents users have in a music streaming recommendation setting. Additionally, we identify the importance of shared learning across intents and propose a multi-level hierarchical model for user satisfaction prediction that leverages user intent information alongside interaction signals. Our findings from extensive experiments on a large scale real world data demonstrate (i) the utility of considering different interaction signals, (ii) the role of intents in interpreting user interactions and (iii) the interplay between interaction signals and intents in predicting user satisfaction.
User intent, behaviour, and perceived satisfaction in product search
Authors: Ning Su, Jiyin He, Yiqun Liu, Min Zhang, Shaoping Ma
Venue: Conference WSDM 2018
Year: 2018

As online shopping becomes increasingly popular, users perform more product search to purchase items. Previous studies have investigated people's online shopping behaviours and ways to predict online purchases. However, from a user perspective, there still lacks an in-depth understanding of why users search, how they interact with, and perceive the product search results. In this paper, we address the following three questions: (1) what are the intents of users underlying their search activities? (2) do users behave differently under different search intents? and (3) how does user perceived satisfaction relate to their search behaviour as well as search intents, and can we predict product search satisfaction with interaction signals? Based on an online survey and search logs collected from a major commercial product search engine, we show that user intents in product search fall into three categories: Target Finding (TF), Decision Making (DM) and Exploration (EP). Through a log analysis and a user study, we observe different user interaction patterns as well as perceived satisfaction under these three intents. Using a series of user interaction features, we demonstrate that we can effectively predict user satisfaction, especially for TF and DM intents.
The influence of image search intents on user behavior and satisfaction
Authors: Zhijing Wu, Yiqun Liu, Qianfan Zhang, Kailu Wu, Min Zhang, Shaoping Ma
Venue: Conference WSDM 2019
Year: 2019

Understanding search intents behind queries is of vital importance for improving search performance or designing better evaluation metrics. Although there exist many efforts in Web search user intent taxonomies and investigating how users' interaction behaviors vary with the intent types, only a few of them have been made specifically for the image search scenario. Different from previous works which investigate image search user behavior and task characteristics based on either lab studies or large scale log analysis, we conducted a field study which lasts one month and involves 2,040 search queries from 555 search tasks. By this means, we collected relatively large amount of practical search behavior data with extensive first-tier annotation from users. With this data set, we investigate how various image search intents affect users' search behavior, and try to adopt different signals to predict search satisfaction under the certain intent. Meanwhile, external assessors were also employed to categorize each search task using four orthogonal intent taxonomies. Based on the hypothesis that behavior is dependent of task type, we analyze user search behavior on the field study data, examining characteristics of the session, click and mouse patterns. We also link the search satisfaction prediction to image search intent, which shows that different types of signals play different roles in satisfaction prediction as intent varies. Our findings indicate the importance of considering search intent in user behavior analysis and satisfaction prediction in image search.
Modeling Long- Range Context for Concurrent Dialogue Acts Recognition
Authors: Yue Yu, Siyao Peng, Grace Hui Yang
Venue: Conference CIKM 2019
Year: 2019

In dialogues, an utterance is a chain of consecutive sentences produced by one speaker which ranges from a short sentence to a thousand-word post. When studying dialogues at the utterance level, it is not uncommon that an utterance would serve multiple functions. For instance, "Thank you. It works great." expresses both gratitude and positive feedback in the same utterance. Multiple dialogue acts (DA) for one utterance breeds complex dependencies across dialogue turns. Therefore, DA recognition challenges a model's predictive power over long utterances and complex DA context. We term this problem Concurrent Dialogue Acts (CDA) recognition. Previous work on DA recognition either assumes one DA per utterance or fails to realize the sequential nature of dialogues. In this paper, we present an adapted Convolutional Recurrent Neural Network (CRNN) which models the interactions between utterances of long-range context. Our model significantly outperforms existing work on CDA recognition on a tech forum dataset.
Entity set search of scientific literature: An unsupervised ranking approach
Authors: Jiaming Shen, Jinfeng Xiao, Xinwei He, Jingbo Shang, Saurabh Sinha, Jiawei Han
Venue: Conference SIGIR 2018
Year: 2018

Literature search is critical for any scientific research. Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types. Entity-set queries reflect user's need for finding documents that contain multiple entities and reveal inter-entity relationships and thus pose non-trivial challenges to existing search algorithms that model each entity separately. However, entity-set queries are usually sparse (i.e., not so repetitive), which makes ineffective many supervised ranking models that rely heavily on associated click history. To address these challenges, we introduce SetRank, an unsupervised ranking framework that models inter-entity relationships and captures entity type information. Furthermore, we develop a novel unsupervised model selection algorithm, based on the technique of weighted rank aggregation, to automatically choose the parameter settings in SetRank without resorting to a labeled validation set. We evaluate our proposed unsupervised approach using datasets from TREC Genomics Tracks and Semantic Scholar's query log. The experiments demonstrate that SetRank significantly outperforms the baseline unsupervised models, especially on entity-set queries, and our model selection algorithm effectively chooses suitable parameter settings.
Temporal QoS-aware Web service recommendation via non-negative tensor factorization
Authors: Wancai Zhang, Hailong Sun, Xudong Liu, Xiaohui Guo
Venue: Conference WWW 2014
Year: 2014

With the rapid growth of Web Service in the past decade, the issue of QoS-aware Web service recommendation is becoming more and more critical. Since the Web service QoS information collection work requires much time and effort, and is sometimes even impractical, the service QoS value is usually missing. There are some work to predict the missing QoS value using traditional collaborative filtering methods based on user-service static model. However, the QoS value is highly related to the invocation context (e.g., QoS value are various at different time). By considering the third dynamic context information, a Temporal QoS-aware Web Service Recommendation Framework is presented to predict missing QoS value under various temporal context. Further, we formalize this problem as a generalized tensor factorization model and propose a Non-negative Tensor Factorization (NTF) algorithm which is able to deal with the triadic relations of user-service-time model. Extensive experiments are conducted based on our real-world Web service QoS dataset collected on Planet-Lab, which is comprised of service invocation response-time and throughput value from 343 users on 5817 Web services at 32 time periods. The comprehensive experimental analysis shows that our approach achieves better prediction accuracy than other approaches.
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
Authors: Zahra Nazari, Christophe Charbuillet, Johan Pages, Martin Laurent, Denis Charrier, Briana Vecchione, Ben Carterette
Venue: Conference SIGIR 2020
Year: 2020

Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50\% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.
Debiasing vandalism detection models at wikidata
Authors: Stefan Heindorf, Yan Scholten, Gregor Engels, Martin Potthast
Venue: Conference WWW 2019
Year: 2019

Crowdsourced knowledge bases like Wikidata suffer from low-quality edits and vandalism, employing machine learning-based approaches to detect both kinds of damage. We reveal that state-of-the-art detection approaches discriminate anonymous and new users: benign edits from these users receive much higher vandalism scores than benign edits from older ones, causing newcomers to abandon the project prematurely. We address this problem for the first time by analyzing and measuring the sources of bias, and by developing a new vandalism detection model that avoids them. Our model FAIR-S reduces the bias ratio of the state-of-the-art vandalism detector WDVD from 310.7 to only 11.9 while maintaining high predictive performance at 0.963 ROC and 0.316 PR.
Computing Views of OWL Ontologies for the Semantic Web
Authors: Jiaqi Li, Xuan Wu, Chang Lu, Wenxing Deng, Yizheng Zhao
Venue: Conference WWW 2021
Year: 2021

This paper tackles the problem of computing views of OWL ontologies using a forgetting-based approach. In traditional relational databases, a view is a subset of a database, whereas in ontologies, a view is more than a subset; it contains not only axioms contained in the original ontology, but may also contain newly-derived axioms entailed by the original ontology (implicitly contained in the original ontology). Specifically, given an ontology , the signature of is the set of all the names in , and a view of is a new ontology obtained from using only part of ’s signature, namely the target signature, while preserving all logical entailments up to the target signature. Computing views of OWL ontologies is useful for Semantic Web applications such as ontology-based query answering, in a way that the view can be used as a substitute of the original ontology to answer queries formulated with the target signature, and information hiding, in the sense that it restricts users from viewing certain information of an ontology. Forgetting is a form of non-standard reasoning concerned with eliminating from an ontology a subset of its signature, namely the forgetting signature, in such a way that all logical entailments are preserved up to the target signature. Forgetting can thus be used as a means for computing views of OWL ontologies — the solution of forgetting a set of names from an ontology is the view of for the target signature . In this paper, we present a forgetting-based method for computing views of OWL ontologies specified in the description logic , the basic extended with role hierarchy, nominals and inverse roles. The method is terminating and sound. Despite the method not being complete, an evaluation with a prototype implementation of the method on a corpus of real-world ontologies has shown very good success rates. This is very useful from the perspective of the Semantic Web, as it provides knowledge engineers with a powerful tool for creating views of OWL ontologies.
Enabling Automatic Discovery and Querying of Web APIs at Web Scale using Linked Data Standards
Authors: Franck Michel, Catherine Faron-Zucker, Olivier Corby, Fabien Gandon
Venue: Conference WWW 2019
Year: 2019

To help in making sense of the ever-increasing number of data sources available on the Web, in this article we tackle the problem of enabling automatic discovery and querying of data sources at Web scale. To pursue this goal, we suggest to (1) provision rich descriptions of data sources and query services thereof, (2) leverage the power of Web search engines to discover data sources, and (3) rely on simple, well-adopted standards that come with extensive tooling. We apply these principles to the concrete case of SPARQL micro-services that aim at querying Web APIs using SPARQL. The proposed solution leverages SPARQL Service Description, SHACL, DCAT, VoID, Schema.org and Hydra to express a rich functional description that allows a software agent to decide whether a micro-service can help in carrying out a certain task. This description can be dynamically transformed into a Web page embedding rich markup data. This Web page is both a human-friendly documentation and a machine-readable description that makes it possible for humans and machines alike to discover and invoke SPARQL micro-services at Web scale, as if they were just another data source. We report on a prototype implementation that is available on-line for test purposes, and that can be effectively discovered using Google’s Dataset Search engine.
Testing dynamic incentive compatibility in display ad auctions
Authors: Yuan Deng, Sébastien Lahaie
Venue: Conference KDD 2019
Year: 2019

The question of transparency has become a key point of contention between buyers and sellers of display advertising space: ads are allocated via complex, black-box auction systems whose mechanics can be difficult to model let alone optimize against. Motivated by this concern, this paper takes the perspective of a single advertiser and develops statistical tests to confirm whether an underlying auction mechanism is dynamically incentive compatible (IC), so that truthful bidding in each individual auction and across time is an optimal strategy. The most general notion of dynamic-IC presumes that the seller knows how buyers discount future surplus, which is questionable in practice. We characterize dynamic mechanisms that are dynamic-IC for all possible discounting factors according to two intuitive conditions: the mechanism should be IC at each stage in the usual sense, and expected present utility (under truthful bidding) should be independent of past bids. The conditions motivate two separate experiments based on bid perturbations that can be run simultaneously on the same impression traffic. We provide a novel statistical test of stage-IC along with a test for utility-independence that can detect lags in how the seller uses past bid information. We evaluate our tests on display ad data from a major ad exchange and show how they can accurately uncover evidence of first- or second-price auctions coupled with dynamic reserve prices, among other types of dynamic mechanisms.
Restoring Healthy Online Discourse by Detecting and Reducing Controversy, Misinformation, and Toxicity Online
Authors: Shiri Dori-Hacohen, Keen Sung, Jengyu Chou, Julian Lustig-Gonzalez
Venue: Conference SIGIR 2021
Year: 2021

Healthy online discourse is becoming less and less accessible beneath the growing noise of controversy, mis- and dis-information, and toxic speech. While IR is crucial in detecting harmful speech, researchers must work across disciplines to develop interventions, and partner with industry to deploy them rapidly and effectively. In this position paper, we argue that both detecting online information disorders and deploying novel, real-world content moderation tools is crucial in promoting empathy in social networks, and maintaining free expression and discourse. We detail our insights in studying different social networks such as Parler and Reddit. Finally, we discuss the joys and challenges as a lab-grown startup working with both academia and other industrial partners in finding a path toward a better, more trustworthy online ecosystem.