OpenRaiser/Intern-Atlas huggingface dataset:
Fact-checking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks
Authors: Marcella Tambuscio, Giancarlo Ruffo, Alessandro Flammini, Filippo Menczer
Venue: Conference WWW 2015
Year: 2015

spread of misinformation, rumors and hoaxes. The goal of this work is to introduce a simple modeling framework to study the diffusion of hoaxes and in particular how the availability of debunking information may contain their diffusion. As traditionally done in the mathematical modeling of information diffusion processes, we regard hoaxes as viruses: users can become infected if they are exposed to them, and turn into spreaders as a consequence. Upon verification, users can also turn into non-believers and spread the same attitude with a mechanism analogous to that of the hoax-spreaders. Both believers and non-believers, as time passes, can return to a susceptible state. Our model is characterized by four parameters: spreading rate, gullibility, probability to verify a hoax, and that to forget one's current belief. Simulations on homogeneous, heterogeneous, and real networks for a wide range of parameters values reveal a threshold for the fact-checking probability that guarantees the complete removal of the hoax from the network. Via a mean field approximation, we establish that the threshold value does not depend on the spreading rate but only on the gullibility and forgetting probability. Our approach allows to quantitatively gauge the minimal reaction necessary to eradicate a hoax.
Category-Driven Approach for Local Related Business Recommendations
Authors: Yonathan Perez, Michael Schueppert, Matthew Lawlor, Shaunak Kishore
Venue: Conference CIKM 2015
Year: 2015

When users search online for a business, the search engine may present them with a list of related business recommendations. We address the problem of constructing a useful and diverse list of such recommendations that would include an optimal combination of substitutes and complements. Substitutes are similar potential alternatives to the searched business, whereas complements are local businesses that can offer a more comprehensive and better rounded experience for a user visiting the searched locality. In our problem setting, each business belongs to a category in an ontology of business categories. Two businesses are defined as substitutes of one another if they belong to the same category, and as complements if they are otherwise relevant to each other.
Sequential-based Adversarial Optimisation for Personalised Top-N Item Recommendation
Authors: Jarana Manotumruksa, Emine Yilmaz
Venue: Conference SIGIR 2020
Year: 2020

Personalised top-N item recommendation systems aim to generate a ranked list of interesting items to users based on their interactions (e.g. click, purchase and rating). Recently, various sequential-based factorised approaches have been proposed to exploit deep neural networks to effectively capture the users' dynamic preferences from their sequences of interactions. These factorised approaches usually rely on a pairwise ranking objective such as the Bayesian Personalised Ranking (BPR) for optimisation. However, previous works have shown that optimising factorised approaches with BPR can hinder the generalisation, which can degrade the quality of item recommendations. To address this challenge, we propose a Sequential-based Adversarial Optimisation (SAO) framework that effectively enhances the generalisation of sequential-based factorised approaches. Comprehensive experiments on six public datasets demonstrate the effectiveness of the SAO framework in enhancing the performance of the state-of-the-art sequential-based factorised approach in terms of NDCG by 3-14%.
When sheep shop: measuring herding effects in product ratings with natural experiments
Authors: Gael Lederrey, Robert West
Venue: Conference WWW 2018
Year: 2018

As online shopping becomes ever more prevalent, customers rely increasingly on product rating websites for making purchase decisions. The reliability of online ratings, however, is potentially compromised by the so-called herding effect: when rating a product, customers may be biased to follow other customers' previous ratings of the same product. This is problematic because it skews long-term customer perception through haphazard early ratings. The study of herding poses methodological challenges. In particular, observational studies are impeded by the lack of counterfactuals: simply correlating early with subsequent ratings is insufficient because we cannot know what the subsequent ratings would have looked like had the first ratings been different. The methodology introduced here exploits a setting that comes close to an experiment, although it is purely observational---a natural experiment. Our key methodological device consists in studying the same product on two separate rating sites, focusing on products that received a high first rating on one site, and a low first rating on the other. This largely controls for confounds such as a product»s inherent quality, advertising, and producer identity, and lets us isolate the effect of the first rating on subsequent ratings. In a case study, we focus on beers as products and jointly study two beer rating sites, but our method applies to any pair of sites across which products can be matched. We find clear evidence of herding in beer ratings. For instance, if a beer receives a very high first rating, its second rating is on average half a standard deviation higher, compared to a situation where the identical beer receives a very low first rating. Moreover, herding effects tend to last a long time and are noticeable even after 20 or more ratings. Our results have important implications for the design of better rating systems.
Refined experts: improving classification in large taxonomies
Authors: Paul N. Bennett, Nam Nguyen
Venue: Conference SIGIR 2009
Year: 2009

While large-scale taxonomies--especially for web pages--have been in existence for some time, approaches to automatically classify documents into these taxonomies have met with limited success compared to the more general progress made in text classification. We argue that this stems from three causes: increasing sparsity of training data at deeper nodes in the taxonomy, error propagation where a mistake made high in the hierarchy cannot be recovered, and increasingly complex decision surfaces in higher nodes in the hierarchy. While prior research has focused on the first problem, we introduce methods that target the latter two problems--first by biasing the training distribution to reduce error propagation and second by propagating up "first-guess" expert information in a bottom-up manner before making a refined top down choice. Finally, we present an empirical study demonstrating that the suggested changes lead to 10--30% improvements in F1 scores versus an accepted competitive baseline, hierarchical SVMs.
Extracting shared subspace for multi-label classification
Authors: Shuiwang Ji, Lei Tang, Shipeng Yu, Jieping Ye
Venue: Conference KDD 2008
Year: 2008

Multi-label problems arise in various domains such as multi-topic document categorization and protein function prediction. One natural way to deal with such problems is to construct a binary classifier for each label, resulting in a set of independent binary classification problems. Since the multiple labels share the same input space, and the semantics conveyed by different labels are usually correlated, it is essential to exploit the correlation information contained in different labels. In this paper, we consider a general framework for extracting shared structures in multi-label classification. In this framework, a common subspace is assumed to be shared among multiple labels. We show that the optimal solution to the proposed formulation can be obtained by solving a generalized eigenvalue problem, though the problem is non-convex. For high-dimensional problems, direct computation of the solution is expensive, and we develop an efficient algorithm for this case. One appealing feature of the proposed framework is that it includes several well-known algorithms as special cases, thus elucidating their intrinsic relationships. We have conducted extensive experiments on eleven multi-topic web page categorization tasks, and results demonstrate the effectiveness of the proposed formulation in comparison with several representative algorithms.
Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval
Authors: Ruey-Cheng Chen, Luke Gallagher, Roi Blanco, J. Shane Culpepper
Venue: Conference SIGIR 2017
Year: 2017

Complex machine learning models are now an integral part of modern, large-scale retrieval systems. However, collection size growth continues to outpace advances in efficiency improvements in the learning models which achieve the highest effectiveness. In this paper, we re-examine the importance of tightly integrating feature costs into multi-stage learning-to-rank (LTR) IR systems. We present a novel approach to optimizing cascaded ranking models which can directly leverage a variety of different state-of-the-art LTR rankers such as LambdaMART and Gradient Boosted Decision Trees. Using our cascade model, we conclusively show that feature costs and the number of documents being re-ranked in each stage of the cascade can be balanced to maximize both efficiency and effectiveness. Finally, we also demonstrate that our cascade model can easily be deployed on commonly used collections to achieve state-of-the-art effectiveness results while only using a subset of the features required by the full model.
Counterfactual Collaborative Reasoning
Authors: Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang
Venue: Conference WSDM 2023
Year: 2023

Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability--counterfactual reasoning and (neural) logical reasoning--we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate "difficult" counterfactual training examples for data augmentation, which--together with the original training examples--can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on "implicit data augmentation" over users' implicit feedback, while our framework conducts "explicit data augmentation" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations.
tdGraphEmbed: Temporal dynamic graph-level embedding
Authors: Moran Beladev, Lior Rokach, Gilad Katz, Ido Guy, Kira Radinsky
Venue: Conference CIKM 2020
Year: 2020

Temporal dynamic graphs are graphs whose topology evolves over time, with nodes and edges added and removed between different time snapshots. Embedding such graphs in a low-dimensional space is important for a variety of tasks, including graphs' similarities, time series trends analysis and anomaly detection, graph visualization, graph classification, and clustering. Despite the importance of the temporal element in these tasks, existing graph embedding methods focus on capturing the graph's nodes in a static mode and/or do not model the graph in its entirety in temporal dynamic mode. In this study, we present tdGraphEmbed, a novel temporal graph-level embedding approach that extend the random-walk based node embedding methods to globally embed both the nodes of the graph and its representation at each time step, thus creating representation of the entire graph at each step. Our approach was applied to graph similarity ranking, temporal anomaly detection, trend analysis, and graph visualizations tasks, where we leverage our temporal embedding in a fast and scalable way for each of the tasks. An evaluation of tdGraphEmbed on five real-world datasets shows that our approach can outperform state-of-the-art approaches used for graph embedding and node embedding in temporal graphs.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Authors: Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
Venue: Conference KDD 2022
Year: 2022

Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed. However, how to take action to address these patterns is not always clear. In a collaboration between ML and human-computer interaction researchers, physicians, and data scientists, we develop GAM Changer, the first interactive system to help domain experts and data scientists easily and responsibly edit Generalized Additive Models (GAMs) and fix problematic patterns. With novel interaction techniques, our tool puts interpretability into action--empowering users to analyze, validate, and align model behaviors with their knowledge and values. Physicians have started to use our tool to investigate and fix pneumonia and sepsis risk prediction models, and an evaluation with 7 data scientists working in diverse domains highlights that our tool is easy to use, meets their model editing needs, and fits into their current workflows. Built with modern web technologies, our tool runs locally in users' web browsers or computational notebooks, lowering the barrier to use. GAM Changer is available at the following public demo link: https://interpret.ml/gam-changer.
Structuring Wikipedia articles with section recommendations
Authors: Tiziano Piccardi, Michele Catasta, Leila Zia, Robert West
Venue: Conference SIGIR 2018
Year: 2018

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.
Fast RFM model for customer segmentation
Authors: Shicheng Wan, Jiahui Chen, Zhenlian Qi, Wensheng Gan, Linlin Tang
Venue: Conference WWW 2022
Year: 2022

With booming e-commerce and World Wide Web (WWW), a powerful tool in customer relationship management (CRM), called the RFM analysis model, has been used to ensure that major enterprises make more profit. Combined with data mining technologies, the CRM system can automatically predict the future behavior of customers to raise customer retention rate. However, a key issue is that the existing RFM analysis models are not efficient enough. Thus, in this study, a fast algorithm based on a compact list-based data structure is proposed along with several efficient pruning strategies to address this issue. The new algorithm considers recency (R), frequency (F), and monetary/utility (M) as three different thresholds to discover interesting patterns where the R, F, and M thresholds combined are no less than the user-specified minimum values. More significantly, the downward-closure property of frequency and monetary metrics are utilized to discover super-itemsets. Then, an extensive experimental study demonstrated that the algorithm outperforms state-of-the-art algorithms on various datasets. It is also demonstrated that the proposed algorithm performs well when considering the frequency metric alone.
OptiRef: Query Optimization for Knowledge Bases
Authors: Wafaa El Husseini, Cheikh Brahim El Vaigh, François Goasdoué, Hélène Jaudoin
Venue: Conference WWW 2023
Year: 2023

Ontology-mediated query answering (OMQA) consists in asking database queries on a knowledge base (KB); a KB is a set of facts, the KB’s database, described by domain knowledge, the KB’s ontology. FOL-rewritability is the main OMQA technique: it reformulates a query w.r.t. the KB’s ontology so that the evaluation of the reformulated query on the KB’s database computes the correct answers. However, because this technique embeds the domain knowledge relevant to the query into the reformulated query, a reformulated query may be complex and its optimization is the crux of efficiency. We showcase OptiRef that implements a novel, general optimization framework for efficient query answering on datalog ±, description logic, existential rules, OWL and RDF/S KBs. OptiRef optimizes reformulated queries by rapidly computing, based on a KB’s database summary, simpler (contained) queries with the same answers. We demonstrate OptiRef’s effectiveness on well-established benchmarks: performance is significantly improved in general, up to several orders of magnitude in the best cases!
Computing personalized pageranks
Authors: Franco Scarselli, Ah Chung Tsoi, Markus Hagenbuchner
Venue: Conference WWW 2004
Year: 2004

A recently published approach to adaptive page rank, using the solution of quadratic optimization methods with a set of simple constraints, is modified to permit classification of web pages according to their page contents, URLs. This modification allows the approach to be more adapted to the needs of focussed crawlers, or personalized search engines.
Efficient implicit unsupervised text hashing using adversarial autoencoder
Authors: Khoa D. Doan, Chandan K. Reddy
Venue: Conference WWW 2020
Year: 2020

Searching for documents with semantically similar content is a fundamental problem in the information retrieval domain with various challenges, primarily, in terms of efficiency and effectiveness. Despite the promise of modeling structured dependencies in documents, several existing text hashing methods lack an efficient mechanism to incorporate such vital information. Additionally, the desired characteristics of an ideal hash function, such as robustness to noise, low quantization error and bit balance/uncorrelation, are not effectively learned with existing methods. This is because of the requirement to either tune additional hyper-parameters or optimize these heuristically and explicitly constructed cost functions. In this paper, we propose a Denoising Adversarial Binary Autoencoder (DABA) model which presents a novel representation learning framework that captures structured representation of text documents in the learned hash function. Also, adversarial training provides an alternative direction to implicitly learn a hash function that captures all the desired characteristics of an ideal hash function. Essentially, DABA adopts a novel single-optimization adversarial training procedure that minimizes the Wasserstein distance in its primal domain to regularize the encoder’s output of either a recurrent neural network or a convolutional autoencoder. We empirically demonstrate the effectiveness of our proposed method in capturing the intrinsic semantic manifold of the related documents. The proposed method outperforms the current state-of-the-art shallow and deep unsupervised hashing methods for the document retrieval task on several prominent document collections.
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases
Authors: Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum
Venue: Conference WSDM 2022
Year: 2022

Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, QA systems first reduce the answer search space by identifying a set of facts that is likely to contain all answers and relevant cues. The most common technique for doing this is to apply named entity disambiguation (NED) systems to the question, and retrieve KB facts for the disambiguated entities. This work presents CLOCQ, an efficient method that prunes irrelevant parts of the search space using KB-aware signals. CLOCQ uses a top-k query processor over score-ordered lists of KB items that combine signals about lexical matching, relevance to the question, coherence among candidate items, and connectivity in the KB graph. Experiments with two recent QA benchmarks for complex questions demonstrate the superiority of CLOCQ over state-of-the-art baselines with respect to answer presence, size of the search space, and runtimes.
Pretraining representations of multi-modal multi-query e-commerce search
Authors: Xinyi Liu, Wanxian Guan, Lianyun Li, Hui Li 0057, Chen Lin 0001, Xubin Li, Si Chen 0010, Jian Xu 0015, Hongbo Deng, Bo Zheng 0007
Venue: Conference KDD 2022
Year: 2022

The importance of modeling contextual information within a search session has been widely acknowledged. However, learning representations of multi-query multi-modal (MM) search, in which Mobile Taobao users repeatedly submit textual and visual queries, remains unexplored in literature. Previous work which learns task-specific representations of textual query sessions fails to capture diverse query types and correlations in MM search sessions. This paper presents to represent MM search sessions by heterogeneous graph neural network (HGN). A multi-view contrastive learning framework is proposed to pretrain the HGN, with two views to model different intra-query, inter-query, and inter-modality information diffusion in MM search. Extensive experiments demonstrate that, the pretrained session representation can benefit state-of-the-art baselines on various downstream tasks, such as personalized click prediction, query suggestion, and intent classification.
Online resource allocation in markov chains
Authors: Jianhao Jia, Hao Li, Kai Liu, Ziqi Liu, Jun Zhou, Nikolai Gravin, Zhihao Gavin Tang
Venue: Conference WWW 2023
Year: 2023

A large body of work in Computer Science and Operations Research study online algorithms for stochastic resource allocation problems. The most common assumption is that the online requests have randomly generated i.i.d. types. This assumption is well justified for static markets and/or relatively short time periods. We consider dynamic markets, whose states evolve as a random walk in a market-specific Markov Chain. This is a new model that generalizes previous i.i.d. settings. We identify important parameters of the Markov chain that is crucial for obtaining good approximation guarantees to the expected value of the optimal offline algorithm which knows realizations of all requests in advance. We focus on a stylized single-resource setting and: (i) generalize the well-known Prophet Inequality from the optimal stopping theory (single-unit setting) to Markov Chain setting; (ii) in multi-unit setting, design a simple algorithm that is asymptotically optimal under mild assumptions on the underlying Markov chain.