OpenRaiser/Intern-Atlas huggingface dataset:
Improving Bounce Rate Prediction for Rare Queries by Leveraging Landing Page Signals
Authors: Yeshi Dolma, Raunak Kalani, Astha Agrawal, Saurav Basu
Venue: WWW
Year: 2021

Bounce rate prediction for clicked ads in sponsored search advertising is crucial for improving the quality of ads shown to the user. Bounce rate represents the proportion of landing pages for clicked ads on which users spend less than a specified time signifying that the user did not find a possible match of their query intent with the landing page content. In the pay-per-click revenue model for search engines, higher bounce rates mean advertisers get charged without meaningful user engagement, which impacts user and advertiser retention in long term. In real-time search engine settings complex ML models are prohibitive due to stringent latency requirements. Also historical logs are ineffective for rare queries (tail) where the data is sparse, as well as for matching user intent to adcopy when the query and bidded keywords don’t exactly overlap (smart match). In this paper, we propose a real-time bounce rate prediction system that leverages lightweight features like modified tf, positional and proximity features computed from ad landing pages and improves prediction for rare queries. The model preserves privacy and uses no user based feature. The entire ensemble is trained on millions of examples from the offline user log of the Bing commercial search engine and improves the ranking metrics for tail queries and smart match by more than 2x compared to a model that only uses ad-copy-advertiser features.
Beyond Text and Back Again
Authors: Desmond Elliott
Venue: WWW
Year: 2021

A talk with two parts covering three modalities. In the first part, I will talk about NLP Beyond Text, where we integrate visual context into a speech recognition model and find that the recovery of different types of masked speech inputs is improved by fine-grained visual grounding against detected objects [2]. In the second part, I will come Back Again, and talk about the benefits of textual supervision in cross-modal speech–vision retrieval models [1].
A Linguistic Study on Relevance Modeling in Information Retrieval
Authors: Yixing Fan, Jiafeng Guo, Xinyu Ma, Ruqing Zhang, Yanyan Lan, Xueqi Cheng
Venue: WWW
Year: 2021

Relevance plays a central role in information retrieval (IR), which has received extensive studies starting from the 20th century. The definition and the modeling of relevance has always been critical challenges in both information science and computer science research areas. Along with the debate and exploration on relevance, IR has already become a core task in many real-world applications, such as Web search engines, question answering systems, conversational bots, and so on. While relevance acts as a unified concept in all these retrieval tasks, the inherent definitions are quite different due to the heterogeneity of these tasks. This raises a question to us: Do these different forms of relevance really lead to different modeling focuses? To answer this question, in this work, we conduct an empirical study on relevance modeling in three representative IR tasks, i.e., document retrieval, answer retrieval, and response retrieval. Specifically, we attempt to study the following two questions: 1) Does relevance modeling in these tasks really show differences in terms of natural language understanding (NLU)? We employ 16 linguistic tasks to probe a unified retrieval model over these three retrieval tasks to answer this question. 2) If there do exist differences, how can we leverage the findings to enhance the relevance modeling? We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. We believe the way we study the problem as well as our findings would be beneficial to the IR community.
Data Poisoning Attacks and Defenses to Crowdsourcing Systems
Authors: Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian, Jia Liu
Venue: WWW
Year: 2021

A key challenge of big data analytics is how to collect a large volume of (labeled) data. Crowdsourcing aims to address this challenge via aggregating and estimating high-quality data (e.g., sentiment label for text) from pervasive clients/users. Existing studies on crowdsourcing focus on designing new methods to improve the aggregated data quality from unreliable/noisy clients. However, the security aspects of such crowdsourcing systems remain under-explored to date. We aim to bridge this gap in this work. Specifically, we show that crowdsourcing is vulnerable to data poisoning attacks, in which malicious clients provide carefully crafted data to corrupt the aggregated data. We formulate our proposed data poisoning attacks as an optimization problem that maximizes the error of the aggregated data. Our evaluation results on one synthetic and two real-world benchmark datasets demonstrate that the proposed attacks can substantially increase the estimation errors of the aggregated data. We also propose two defenses to reduce the impact of malicious clients. Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.
PDTM: Poisson Distribution-based Trust Model for Web of Things
Authors: Weidong Fang, Wuxiong Zhang, Wei Chen, Li Yi, Weiwei Gao
Venue: WWW
Year: 2021

Web of things (WoT) are inclined to suffer from internal attacks, which are from compromised nodes. Due to resource-constraint of WoT, the traditional security methods cannot be deployed. One of the most appropriate protection mechanisms to resist internal attacks is the trust management system. For the sake of evaluate the performance of WoT reasonably and appropriately, we improve the Beta-based reputation system, and propose a Poisson Distribution-based trust model (PDTM) in this paper. In view of evaluating a sensor node (or terminal) behaviors, its reputation and trust are represented by the Poisson distribution. PDTM is used to look for reliable nodes to transmit data and weaken malicious attacks within WoTs. The simulation results indicate that the PDTM can resist internal attack effectively, in order to strengthen the network security.
Grounding Language in Visual and Conversational Contexts
Authors: Raquel Fernández
Venue: WWW
Year: 2021

Most language use is driven by specific communicative goals in interactive setups, where often visual perception goes hand in hand with language processing. I will discuss some recent projects by my research group related to modelling language generation in socially and visually grounded contexts, arguing that such models can help us to better understand the cognitive processes underpinning these abilities in humans and contribute to more human-like conversational agents.
FAIR Linked Data - Towards a Linked Data Backbone for Users and Machines
Authors: Johannes Frey, Sebastian Hellmann
Venue: WWW
Year: 2021

Although many FAIR principles could be fulfilled by 5-star Linked Open Data, the successful realization of FAIR poses a multitude of challenges. FAIR publishing and retrieval of Linked Data is still rather a FAIRytale than reality, for users and machines. In this paper, we give an overview on four major approaches that tackle individual challenges of FAIR data and present our vision of a FAIR Linked Data backbone. We propose 1) DBpedia Databus - a flexible, heavily automatable dataset management and publishing platform based on DataID metadata; that is extended by 2) the novel Databus Mods architecture which allows for flexible, unified, community-specific metadata extensions and (search/annotation) overlay systems; 3) DBpedia Archivo an archiving solution for unified handling and improvement of FAIRness for ontologies on publisher and consumer side; as well as 4) the DBpedia Global ID management and lookup services to cluster and discover equivalent entities and properties
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models
Authors: Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy, Gerhard Weikum
Venue: WWW
Year: 2021

System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called Elixir, where user feedback on explanations is leveraged for pairwise learning of user preferences. Elixir leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.
Transitive Power Modeling for Improving Resource Efficiency in a Hyperscale Datacenter
Authors: Alexander Gilgur, Brian Coutinho, Iyswarya Narayanan, Parth Malani
Venue: WWW
Year: 2021

Maintaining efficient utilization of allocated compute resources and controlling their capital and operating expenditure is important for running a hyperscale datacenter infrastructure. Power is one of the most constrained and difficult to manage resources in datacenters. Accurate accounting of power usage across clients of multi-tenant web services can improve budgeting, planning and provisioning of compute resources. In this work, we propose a queuing theory based transitive power modeling framework that estimates the total power cost of a client request across the stack of shared services running in Facebook datacenters. By capturing the non-linearity of power vs load relation, our model is able to estimate marginal change in power consumption of a system upon serving a request with a mean error of less than 4% when applied on production services. In view of the fact that datacenter capacity is planned for peak demand, we test this model at peak load to report up to 2x improvement in accuracy compared to a mathematical model. We further leverage this framework along with a distributed tracing system to estimate power demand shift for serving particular product features within fraction of a percentage and guide the decision to shift their computation at off-peak time.
TCS_WITM_2021 @FinSim-2: Transformer based Models for Automatic Classification of Financial Terms
Authors: Tushar Goel, Vipul Chauhan, Ishan Verma, Tirthankar Dasgupta, Lipika Dey
Venue: WWW
Year: 2021

Recent advancement in neural network architectures has provided several opportunities to develop systems to automatically extract and represent information from domain specific unstructured text sources. The Finsim-2021 shared task, collocated with the FinNLP workshop, offered the challenge to automatically learn effective and precise semantic models of financial domain concepts. Building such semantic representations of domain concepts requires knowledge about the specific domain. Such a thorough knowledge can be obtained through the contextual information available in raw text documents on those domains. In this paper, we proposed a transformer-based BERT architecture that captures such contextual information from a set of domain specific raw documents and then perform a classification task to segregate domain terms into fixed number of class labels. The proposed model not only considers the contextual BERT embeddings but also incorporates a TF-IDF vectorizer that gives a word level importance to the model. The performance of the model has been evaluated against several baseline architectures.
Mobile Positioning Based on TAE-GRU
Authors: Canyang Guo, Ling Wu, Cheng Shi, Chi-Hua Chen
Venue: WWW
Year: 2021

This paper motivates to solve the multiple mapping of Received Signal Strength Indications (RSSIs) and location estimating problem in mobile positioning. A mobile positioning method based on Time-distributed Auto Encoder and Gated Recurrent Unit (TAE-GRU) is proposed to realize the mobile positioning. To distinguish the identical RSSI of different temporal steps, this paper develops a reconstructed model based on Time-distributed Auto Encoder (TAE), which is conducive for further learning of the estimated model. Among them, time-distributed technology is utilized to translate the data of each temporal step separately accommodating the temporal characteristics of RSSI data. Besides, an estimated model based on Gated Recurrent Unit (GRU) is developed to learn the temporal relationship of RSSI data to estimate the locations of mobile devices. Combining the TAE model and GRU model, the proposed model is provided with the capability of solving multiple mapping and mobile positioning dilemma. Massive experimental results demonstrated that the proposed method provides superior performance than comparative methods when solving multiple mapping and positioning problems.
Analysis and Visualisation of Time Series Data on Networks with Pathpy
Authors: Jürgen Hackl, Ingo Scholtes, Luka V. Petrovic, Vincenzo Perri, Luca Verginer, Christoph Gote
Venue: WWW
Year: 2021

The Open Source software package pathpy, available at https://www.pathpy.net, implements statistical techniques to learn optimal graphical models for the causal topology generated by paths in time-series data. Operationalizing Occam’s razor, these models balance model complexity with explanatory power for empirically observed paths in relational time series. Standard network analysis is justified if the inferred optimal model is a first-order network model. Optimal models with orders larger than one indicate higher-order dependencies and can be used to improve the analysis of dynamical processes, node centralities and clusters.
Wait, Let's Think about Your Purchase Again: A Study on Interventions for Supporting Self-Controlled Online Purchases
Authors: Yunha Han, Hwiyeon Kim, Hyeshin Chu, Joohee Kim, Hyunwook Lee, Seunghyeong Choe, Dooyoung Jung, Dongil Chung, Bum Chul Kwon, Sungahn Ko
Venue: WWW
Year: 2021

As online marketplaces adopt new technologies to encourage consumers’ purchases (e.g., one-click purchases), the number of consumers who impulsively buy products also increases. Although some interventions have been introduced for consumers’ self-controlled purchases, there have been few studies that evaluate the effectiveness of the techniques in the real environment. In this paper, we conducted an online survey with 118 consumers in their 20s to investigate their impulse buying behaviors and self-control strategies. Based on the survey results and literature surveys, we developed interventions that can assist consumers in controlling their online purchase habits, including Reflection, Distraction, Desire Reduction, and Salient Cost. For evaluation, we enrolled 107 participants in a user study on a real-world e-commerce site. The results indicate that all interventions were effective in reducing impulse buying urges, with variations in user experiences. Our findings and design implications are discussed.
Information Extraction From Co-Occurring Similar Entities
Authors: Nicolas Heist, Heiko Paulheim
Venue: WWW
Year: 2021

Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or DBpedia are, however, far from being complete. In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. In contrast to existing approaches, we do not focus on relationships within a listing (e.g., between two entities in a table row) but on the relationship between a listing’s subject entities and the context of the listing. To that end, we propose a descriptive rule mining approach that uses distant supervision to derive rules for these relationships based on a listing’s context. Extracted from a suitable data corpus, the rules can be used to extend a knowledge graph with novel entities and assertions. In our experiments we demonstrate that the approach is able to extract up to 3M novel entities and 30M additional assertions from listings in Wikipedia. We find that the extracted information is of high quality and thus suitable to extend Wikipedia-based knowledge graphs like DBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an increase of covered entities by roughly 50%.
Do I Trust this Stranger? Generalized Trust and the Governance of Online Communities
Authors: Jérôme Hergueux, Yann Algan, Yochai Benkler, Mayo Fuster Morell
Venue: WWW
Year: 2021

Online peer production communities such as Wikipedia typically rely on a distinct class of users, called administrators, to enforce cooperation when good faith collaboration fails. Assessing one’s intentions is a complex task, however, especially when operating under time-pressure with a limited number of (costly to collect) cues. In such situations, individuals typically rely on simplifying heuristics to make decisions, at the cost of precision. In this paper, we hypothesize that administrators’ community governance policy might be influenced by general trust attitudes acquired mostly out of the Wikipedia context. We use a decontextualized online experiment to elicit levels of trust in strangers in a sample of 58 English Wikipedia administrators. We show that low-trusting admins exercise their policing rights significantly more (e.g., block about 81% more users than high trusting types on average). We conclude that efficiency gains might be reaped from the further development of tools aimed at inferring users’ intentions from digital trace data.
Extracting Contextualized Quantity Facts from Web Tables
Authors: Vinh Thinh Ho, Koninika Pal, Simon Razniewski, Klaus Berberich, Gerhard Weikum
Venue: WWW
Year: 2021

Quantity queries, with filter conditions on quantitative measures of entities, are beyond the functionality of search engines and QA assistants. To enable such queries over web contents, this paper develops a novel method for automatically extracting quantity facts from ad-hoc web tables. This involves recognizing quantities, with normalized values and units, aligning them with the proper entities, and contextualizing these pairs with informative cues to match sophisticated queries with modifiers. Our method includes a new approach to aligning quantity columns to entity columns. Prior works assumed a single subject-column per table, whereas our approach is geared for complex tables and leverages external corpora as evidence. For contextualization, we identify informative cues from text and structural markup that surrounds a table. For query-time fact ranking, we devise a new scoring technique that exploits both context similarity and inter-fact consistency. Comparisons of our building blocks against state-of-the-art baselines and extrinsic experiments with two query benchmarks demonstrate the benefits of our method.
Online Label Aggregation: A Variational Bayesian Approach
Authors: Chi Hong, Amirmasoud Ghiassi, Yichi Zhou, Robert Birke, Lydia Y. Chen
Venue: WWW
Year: 2021

Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregating results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solutions that can incrementally infer true label distribution via subsets of data items. In this paper, we propose a novel online label aggregation framework, BiLA , which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. BiLA is flexible to accommodate any generating distribution of labels by the exact computation of its posterior distribution. We also derive the convergence bound of the proposed optimizer. We compare BiLA with the state of the art based on minimax entropy, neural networks and expectation maximization algorithms, on synthetic and real-world data sets. Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1.5 percent points for synthetic and real-world datasets, respectively.
Deep Learning meets Knowledge Graphs for Scholarly Data Classification
Authors: Fabian Hoppe, Danilo Dessì, Harald Sack
Venue: WWW
Year: 2021

The amount of scientific literature continuously grows, which poses an increasing challenge for researchers to manage, find and explore research results. Therefore, the classification of scientific work is widely applied to enable the retrieval, support the search of suitable reviewers during the reviewing process, and in general to organize the existing literature according to a given schema. The automation of this classification process not only simplifies the submission process for authors, but also ensures the coherent assignment of classes. However, especially fine-grained classes and new research fields do not provide sufficient training data to automatize the process. Additionally, given the large number of not mutual exclusive classes, it is often difficult and computationally expensive to train models able to deal with multi-class multi-label settings. To overcome these issues, this work presents a preliminary Deep Learning framework as a solution for multi-label text classification for scholarly papers about Computer Science. The proposed model addresses the issue of insufficient data by utilizing the semantics of classes, which is explicitly provided by latent representations of class labels. This study uses Knowledge Graphs as a source of these required external class definitions by identifying corresponding entities in DBpedia to improve the overall classification.
SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series
Authors: Tsung-Yu Hsieh, Yiwei Sun, Xianfeng Tang, Suhang Wang, Vasant G. Honavar
Venue: WWW
Year: 2021

Many applications, e.g., healthcare, education, call for effective methods methods for constructing predictive models from high dimensional time series data where the relationship between variables can be complex and vary over time. In such settings, the underlying system undergoes a sequence of unobserved transitions among a finite set of hidden states. Furthermore, the relationships between the observed variables and their temporal dynamics may depend on the hidden state of the system. To further complicate matters, the hidden state sequences underlying the observed data from different individuals may not be aligned relative to a common frame of reference. Against this background, we consider the novel problem of jointly learning the state-dependent inter-variable relationships as well as the pattern of transitions between hidden states from multi-variate time series data. To solve this problem, we introduce the State-Regularized Vector Autoregressive Model (SrVARM) which combines a state-regularized recurrent neural network to learn the dynamics of transitions between discrete hidden states with an augmented autoregressive model which models the inter-variable dependencies in each state using a state-dependent directed acyclic graph (DAG). We propose an efficient algorithm for training SrVARM by leveraging a recently introduced reformulation of the combinatorial problem of optimizing the DAG structure with respect to a scoring function into a continuous optimization problem. We report results of extensive experiments with simulated data as well as a real-world benchmark that show that SrVARM outperforms state-of-the-art baselines in recovering the unobserved state transitions and discovering the state-dependent relationships among variables.