OpenRaiser/Intern-Atlas huggingface dataset:
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback Based Recommendation
Authors: Lei Chen 0051, Le Wu, Kun Zhang 0015, Richang Hong, Meng Wang 0001
Venue: Conference SIGIR 2021
Year: 2021

As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard'' unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.
An Integrated Bayesian Approach for Effective Multi-Truth Discovery
Authors: Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Lina Yao, Xiaofei Xu, Xue Li
Venue: Conference CIKM 2015
Year: 2015

Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truth-finding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truth-finding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.
Describing and predicting online items with reshare cascades via dual mixture self-exciting processes
Authors: Quyu Kong, Marian-Andrei Rizoiu, Lexing Xie
Venue: Conference CIKM 2020
Year: 2020

It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses these shortcomings by proposing dual mixture self-exciting processes to jointly learn from groups of cascades. We first start from the observation that maximum likelihood estimates for content virality and influence decay are separable in a Hawkes process. Next, our proposed model, which leverages a Borel mixture model and a kernel mixture model, jointly models the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and supplies interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets --- one relating to YouTube videos and the second relating to controversial news articles --- we show that our models capture the differences between online items at the granularity of items, publishers and categories. In particular, we are able to distinguish between far-right, conspiracy, controversial and reputable online news articles based on how they diffuse through social media, achieving an F1 score of 0.945. On holdout datasets, we show that the dual mixture model provides, for reshare diffusion cascades especially unpopular ones, better generalization performance and, for online items, accurate item popularity predictions.
Can I Take a Peek? Continuous Monitoring of Online A/B Tests
Authors: Ramesh Johari
Venue: Conference WWW 2015
Year: 2015

A/B testing is a hallmark of Internet services: from e-commerce sites to social networks to marketplaces, nearly all online services use randomized experiments as a mechanism to make better business decisions. Such tests are generally analyzed using classical frequentist statistical measures: p-values and confidence intervals. Despite their ubiquity, these reported values are computed under the assumption that the experimenter will not continuously monitor their test--in other words, there should be no repeated "peeking" at the results that affects the decision of whether to continue the test. On the other hand, one of the greatest benefits of advances in information technology, computational power, and visualization is precisely the fact that experimenters can watch experiments in progress, with greater granularity and insight over time than ever before.
Is It Enough Just Looking at the Title? Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation
Authors: Taeho Kim, Yungi Kim, Yeon-Chang Lee, Won-Yong Shin, Sang-Wook Kim
Venue: Conference CIKM 2022
Year: 2022

In a news recommender system, a user tends to click on a news article if she is interested in its topic understood by looking at its title. Such a behavior is possible since, when viewing the title, humans naturally think of the contextual meaning of each title word by leveraging their own background knowledge. Motivated by this, we propose a novel personalized news recommendation framework CAST (Context-aware Attention network with a Selection module for Title word representation), which is capable of enriching title words by leveraging body text that fully provides the whole content of a given article as the context. Through extensive experiments, we demonstrate (1) the effectiveness of core modules in CAST, (2) the superiority of CAST over 9 state-of-the-art news recommendation methods, and (3) the interpretability with CAST.
Accurate path-based methods for influence maximization in social networks
Authors: Yun-Yong Ko, Dong-Kyu Chae, Sang-Wook Kim
Venue: Conference WWW 2016
Year: 2016

This paper proposes a novel approach to target-oriented influence estimation, which remedies the drawback of state-of-the-art, thereby understanding information diffusion more accurately in a social network.
Residual convolutional lstm for tweet count prediction
Authors: Hong Wei, Hao Zhou, Jagan Sankaranarayanan, Sudipta Sengupta, Hanan Samet
Venue: Conference WWW 2018
Year: 2018

The tweet count prediction of a local spatial region is to forecast the number of tweets that are likely to be posted from that area over a relatively short period of time. It has many applications such as human mobility analysis, traffic planning, and abnormal event detection. In this paper, we formulate tweet count prediction as a spatiotemporal sequence forecasting problem and design an end-to-end convolutional LSTM based network with skip connection for this problem. Such a model enables us to exploit the unique properties of spatiotemporal data, consisting of not only the temporal characteristics such as temporal closeness, period and trend properties but also spatial dependencies. Our experiments on the city of Seattle, WA as well as a larger city of New York City show that the proposed method consistently outperforms the competitive baseline approaches.
Combining explicit entity graph with implicit text information for news recommendation
Authors: Xuanyu Zhang, Qing Yang, Dongliang Xu
Venue: Conference WWW 2021
Year: 2021

News recommendation is very crucial for online news services to improve user experience and alleviate information overload. Precisely learning representations of news and users is the core problem in news recommendation. Existing models usually focus on implicit text information to learn corresponding representations, which may be insufficient for modeling user interests. Even if entity information is considered from external knowledge, it may still not be used explicitly and effectively for user modeling. In this paper, we propose a novel news recommendation approach, which combine explicit entity graph with implicit text information. The entity graph consists of two types of nodes and three kinds of edges, which represent chronological order, related and affiliation relationship. Then graph neural network is utilized for reasoning on these nodes. Extensive experiments on a real-world dataset, Microsoft News Dataset (MIND), validate the effectiveness of our proposed approach.
Recommending the most effective intervention to improve employment for job seekers with disability
Authors: Ha Xuan Tran, Thuc Duy Le, Jiuyong Li, Lin Liu, Jixue Liu, Yanchang Zhao, Tony Waters
Venue: Conference KDD 2021
Year: 2021

In Disability Employment Services (DES), a growing problem is recommending to disabled job seekers which skill should be upgraded and the best level for upgrading this skill to increase their employment potential most. This problem involves counterfactual reasoning to infer causal effect of factors on employment status to recommend the most effective intervention. Related methods cannot solve our problem adequately since they are developed for non-counterfactual challenges, for binary causal factors, or for randomized trials. In this paper, we present a causality-based method to tackle the problem. The method includes two stages where causal factors of employment status are first detected from data. We then combine a counterfactual reasoning framework with a machine learning approach to build an interpretable model for generating personalized recommendations. Experiments on both synthetic datasets and a real case study from a DES provider show consistent promising performance of improving employability of disabled job seekers. Results from the case study disclose effective factors and their best levels for intervention to increase employability. The most effective intervention varies among job seekers. Our model can separate job seekers by degree of employability increase. This is helpful for DES providers to allocate resources for employment assistance. Moreover, causal interpretability makes our recommendations actionable in DES business practice.
Cobafi: collaborative bayesian filtering
Authors: Alex Beutel, Kenton Murray, Christos Faloutsos, Alexander J. Smola
Venue: Conference WWW 2014
Year: 2014

Given a large dataset of users' ratings of movies, what is the best model to accurately predict which movies a person will like? And how can we prevent spammers from tricking our algorithms into suggesting a bad movie? Is it possible to infer structure between movies simultaneously? In this paper we describe a unified Bayesian approach to Collaborative Filtering that accomplishes all of these goals. It models the discrete structure of ratings and is flexible to the often non-Gaussian shape of the distribution. Additionally, our method finds a co-clustering of the users and items, which improves the model's accuracy and makes the model robust to fraud. We offer three main contributions: (1) We provide a novel model and Gibbs sampling algorithm that accurately models the quirks of real world ratings, such as convex ratings distributions. (2) We provide proof of our model's robustness to spam and anomalous behavior. (3) We use several real world datasets to demonstrate the model's effectiveness in accurately predicting user's ratings, avoiding prediction skew in the face of injected spam, and finding interesting patterns in real world ratings data.
Automatic sequential pattern mining in data streams
Authors: Kouki Kawabata, Yasuko Matsubara, Yasushi Sakurai
Venue: Conference CIKM 2019
Year: 2019

Given a large volume of multi-dimensional data streams, such as that produced by IoT applications, finance and online web-click logs, how can we discover typical patterns and compress them into compact models? In addition, how can we incrementally distinguish multiple patterns while considering the information obtained from a pattern found in a streaming setting? In this paper, we propose a streaming algorithm, namely StreamScope, that is designed to find intuitive patterns efficiently from event streams evolving over time. Our proposed method has the following properties: (a) it is effective: it operates on semi-infinite collections of co-evolving streams and summarizes all the streams into a set of multiple discrete segments grouped by their similarities. (b) it is automatic: it automatically and incrementally recognizes such patterns and generates models for each of them if necessary; (c) it is scalable: the complexity of our method does not depend on the length of the data streams. Our extensive experiments on real data streams demonstrate that StreamScope can find meaningful patterns and achieve great improvements in terms of computational time and memory space over its full batch method competitors.
Deep State-Space Generative Model For Correlated Time-to-Event Predictions
Authors: Yuan Xue, Denny Zhou, Nan Du 0002, Andrew M. Dai, Zhen Xu 0006, Kun Zhang 0043, Claire Cui
Venue: Conference KDD 2020
Year: 2020

Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning. In this work, we propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events (e.g., kidney failure, mortality) by explicitly modeling the temporal dynamics of patients' latent states. Based on these learned patient states, we further develop a new general discrete-time formulation of the hazard rate function to estimate the survival distribution of patients with significantly improved accuracy. Extensive evaluations over real EMR data show that our proposed model compares favorably to various state-of-the-art baselines. Furthermore, our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
HiddenCPG: large-scale vulnerable clone detection using subgraph isomorphism of code property graphs
Authors: Seongil Wi, Sijae Woo, Joyce Jiyoung Whang, Sooel Son
Venue: Conference WWW 2022
Year: 2022

A code property graph (CPG) is a joint representation of syntax, control flows, and data flows of a target application. Recent studies have demonstrated the promising efficacy of leveraging CPGs for the identification of vulnerabilities. It recasts the problem of implementing a specific static analysis for a target vulnerability as a graph query composition problem. It requires devising coarse-grained graph queries that model vulnerable code patterns. Unfortunately, such coarse-grained queries often leave vulnerabilities due to faulty input sanitization undetected. In this paper, we propose, a scalable system designed to identify various web vulnerabilities, including bugs that stem from incorrect sanitization. We designed to find a subgraph in a target CPG that matches a given CPG query having a known vulnerability, which is known as the subgraph isomorphism problem. To address the scalability challenge that stems from the NP-complete nature of this problem, leverages optimization techniques designed to boost the efficiency of matching vulnerable subgraphs. found confirmed vulnerabilities including CVEs among 2,464 potential vulnerabilities in real-world CPGs having a combined total of 1 billion nodes and 1.2 billion edges.
IFSpard: An information fusion-based framework for spam review detection
Authors: Yao Zhu, Hongzhi Liu, Yingpeng Du, Zhonghai Wu
Venue: Conference WWW 2021
Year: 2021

Online reviews, which contain the quality information and user experience about products, always affect the consumption decisions of customers. Unfortunately, quite a number of spammers attempt to mislead consumers by writing fake reviews for some intents. Existing methods for detecting spam reviews mainly focus on constructing discriminative features, which heavily depend on experts and may miss some complex but effective features. Recently, some models attempt to learn the latent representations of reviews, users, and items. However, the learned embeddings usually lack interpretability. Moreover, most of existing methods are based on single classification model while ignoring the complementarity of different classification models. To solve these problems, we propose IFSpard, a novel information fusion-based framework that aims at exploring and exploiting useful information from various aspects for spam review detection. First, we design a graph-based feature extraction method and an interaction-mining-based feature crossing method to automatically extract basic and complex features with consideration of different sources of data. Then, we propose a mutual-information-based feature selection and representation learning method to remove the irrelevant and redundant information contained in the automatically constructed features. Finally, we devise an adaptive ensemble model to make use of the information of constructed features and the abilities of different classifiers for spam review detection. Experimental results on several public datasets show that the proposed model performs better than state-of-the-art methods.
Non-Recursive Cluster-Scale Graph Interacted Model for Click- Through Rate Prediction
Authors: Yuanchen Bei, Hao Chen, Shengyuan Chen, Xiao Huang, Sheng Zhou, Feiran Huang
Venue: Conference CIKM 2023
Year: 2023

Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been shown to enhance Click-Through Rate (CTR) prediction performance. However, online recommender systems impose strict constraints on the inference time of CTR models, which necessitates pruning or filtering users' 1-hop neighbors to reduce computational complexity. Furthermore, while the graph information of users and items has been proven effective in collaborative filtering models, recursive graph convolution can be computationally costly and expensive to implement. To address these challenges, we propose the Non-Recursive Cluster-scale Graph Interacted (NRCGI) model, which reorganizes graph convolutional networks in a non-recursive and cluster-scale view to enable CTR models to consider deep graph information with low computational cost. NRCGI employs non-recursive cluster-scale graph aggregation, which allows the online recommendation computational complexity to shrink from tens of thousands of items to tens to hundreds of clusters. Additionally, since NRCGI aggregates neighbors in a non-recursive view, each hop of neighbors has a clear physical meaning. NRCGI explicitly constructs meaningful interactions between the hops of neighbors of users and items to fully model users' intent towards the given item. Experimental results demonstrate that NRCGI outperforms state-of-the-art baselines in three public datasets and one industrial dataset while maintaining efficient inference.
Text classification and named entities for new event detection
Authors: Giridhar Kumaran, James Allan
Venue: Conference SIGIR 2004
Year: 2004

New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.
Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective
Authors: Teng Xiao, Zhengyu Chen 0001, Suhang Wang
Venue: Conference KDD 2023
Year: 2023

This work studies the problem of learning unbiased algorithms from biased feedback for recommendation. We address this problem from a novel distribution shift perspective. Recent works in unbiased recommendation have advanced the state-of-the-art with various techniques such as re-weighting, multi-task learning, and meta-learning. Despite their empirical successes, most of them lack theoretical guarantees, forming non-negligible gaps between theories and recent algorithms. In this paper, we propose a theoretical understanding of why existing unbiased learning objectives work for unbiased recommendation. We establish a close connection between unbiased recommendation and distribution shift, which shows that existing unbiased learning objectives implicitly align biased training and unbiased test distributions. Built upon this connection, we develop two generalization bounds for existing unbiased learning methods and analyze their learning behavior. Besides, as a result of the distribution shift, we further propose a principled framework, Adversarial Self-Training (AST), for unbiased recommendation. Extensive experiments on real-world and semi-synthetic datasets demonstrate the effectiveness of AST.
A Study of Misinformation in WhatsApp groups with a focus on the Brazilian Presidential Elections
Authors: Caio Machado, Beatriz Kira, Vidya Narayanan, Bence Kollanyi, Philip N. Howard
Venue: Conference WWW 2019
Year: 2019

There are rising concerns over the spread of misinformation in WhatsApp groups and the potential impact on political polarization, hindrance of public debate and fostering acts of political violence. As social media use becomes increasingly widespread, it becomes imperative to study how these platforms can be used to as a tool to spread propaganda and manipulate audience groups ahead of important political events. In this paper, we present a grounded typology to classify links to news sources into different categories including ‘junk’ news sources that deliberately publish or aggregate misleading, deceptive or incorrect information packaged as real news about politics, economics or culture obtained from public WhatsApp groups. Further, we examine a sample of 200 videos and images, extracted from a sample of WhatsApp groups and develop a new typology to classify this media content. For our analysis, we have used data from 130 public WhatsApp groups in the period leading up to the two rounds of the 2018 Brazilian presidential elections.