OpenRaiser/Intern-Atlas huggingface dataset:
Restaurant survival analysis with heterogeneous information
Authors: Jianxun Lian, Fuzheng Zhang, Xing Xie, Guangzhong Sun
Venue: Conference WWW 2017
Year: 2017

For shopkeepers, one of their biggest common concerns is whether their business will thrive or fail in the future. With the development of new ways to collect business data, it is possible to leverage multiple domains' knowledge to build an intelligent model for business assessment. In this paper, we discuss what the potential indicators are for the long-term survival of a physical store. To this end, we study factors from four pillars: geography, user mobility, user rating, and review text. We start by exploring the impact of geographic features, which describe the location environment of the retailer store. The location and nearby places play an important role in the popularity of the shop, and usually less competitiveness and more heterogeneity is better. Then we study user mobility. It can be viewed as supplementary to the geographical placement, showing how the location can attract users from anywhere. Another important factor is how the shop can serve and satisfy users. We find that restaurant survival prediction is a hard task that can not be solved simply using consumers' ratings or sentiment metrics. Compared with conclusive and well-formatted ratings, the various review words provide more insight of the shop and deserve in-depth mining. We adopt several language models to fully explore the textual message. Comprehensive experiments demonstrate that review text indeed have the strongest predictive power. We further compare different cities' models and find the conclusions are highly consistent. Although we focus on the class of restaurant in this paper, the method can be easily extended to other shop categories.
Discovery of fraud rules for telecommunications—challenges and solutions
Authors: Saharon Rosset, Uzi Murad, Einat Neumann, Yizhak Idan, Gadi Pinkas
Venue: Conference KDD 1999
Year: 1999

Many fraud analysis systems have at their heart a rule-based engine for generating alerts about suspicious behaviors. The rules in the system are usually based on expert knowledge. Automatic rule discovery aims at using past examples of fraudulent and legitimate usage to find new patterns and rules to help distinguish between the two. Some aspects of the problem of finding rules suitable for fraud analysis make this problem unique. Among them are the following: the need to find rules combining both the properties of the customer (e.g., credit rating) and properties of the specific "behavior" which indicates fraud (e.g., number of international calls in one day); and the need for a new definition of accuracy: We need to find rules which do not necessarily classify correctly each individual "usage sample" as either fraudulent or not, but ensure the identification, with a minimum of wasted cost and effort, of most of the fraud "cases" (i.e., defrauded customers). These aspects require a special-purpose rule discovery system. We present as an example a two-stage system based on adaptation of the C4.5 rule generator, with an additional rule selection mechanism. Our experimental results indicate that this route is very promising.
Fraudetector: A graph-mining-based framework for fraudulent phone call detection
Authors: Vincent S. Tseng, Jia-Ching Ying, Che-Wei Huang, Yimin Kao, Kuan-Ta Chen
Venue: Conference KDD 2015
Year: 2015

In recent years, fraud is increasing rapidly with the development of modern technology and global communication. Although many literatures have addressed the fraud detection problem, these existing works focus only on formulating the fraud detection problem as a binary classification problem. Due to limitation of information provided by telecommunication records, such classifier-based approaches for fraudulent phone call detection normally do not work well. In this paper, we develop a graph-mining-based fraudulent phone call detection framework for a mobile application to automatically annotate fraudulent phone numbers with a "fraud" tag, which is a crucial prerequisite for distinguishing fraudulent phone calls from normal phone calls. Our detection approach performs a weighted HITS algorithm to learn the trust value of a remote phone number. Based on telecommunication records, we build two kinds of directed bipartite graph: i) CPG and ii) UPG to represent telecommunication behavior of users. To weight the edges of CPG and UPG, we extract features for each pair of user and remote phone number in two different yet complementary aspects: 1) duration relatedness (DR) between user and phone number; and 2) frequency relatedness (FR) between user and phone number. Upon weighted CPG and UPG, we determine a trust value for each remote phone number. Finally, we conduct a comprehensive experimental study based on a dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate the effectiveness of our weighted HITS-based approach and show the strength of taking both DR and FR into account in feature extraction.
Attackability Characterization of Adversarial Evasion Attack on Discrete Data
Authors: Yutong Wang, Yufei Han 0001, Hongyan Bao, Yun Shen, Fenglong Ma, Jin Li 0002, Xiangliang Zhang 0001
Venue: Conference KDD 2020
Year: 2020

Evasion attack on discrete data is a challenging, while practically interesting research topic. It is intrinsically an NP-hard combinatorial optimization problem. Characterizing the conditions guaranteeing the solvability of an evasion attack task thus becomes the key to understand the adversarial threat. Our study is inspired by the weak submodularity theory. We characterize the attackability of a targeted classifier on discrete data in evasion attack by bridging the attackability measurement and the regularity of the targeted classifier. Based on our attackability analysis, we propose a computationally efficient orthogonal matching pursuit-guided attack method for evasion attack on discrete data. It provides provably computational efficiency and attack performances. Substantial experimental results on real-world datasets validate the proposed attackability conditions and the effectiveness of the proposed attack method.
Dynamic Collaborative Recurrent Learning
Authors: Teng Xiao, Shangsong Liang, Zaiqiao Meng
Venue: Conference CIKM 2019
Year: 2019

In this paper, we provide a unified learning algorithm, dynamic collaborative recurrent learning, DCRL, of two directions of recommendations: temporal recommendations focusing on tracking the evolution of users' long-term preference and sequential recommendations focusing on capturing short-term preferences given a short time window. Our DCRL builds based on RNN and Sate Space Model (SSM), and thus it is not only able to collaboratively capture users' short-term and long-term preferences as in sequential recommendations, but also can dynamically track the evolution of users' long-term preferences as in temporal recommendations in a unified framework. In addition, we introduce two smoothing and filtering scalable inference algorithms for DCRL's offline and online learning, respectively, based on amortized variational inference, allowing us to effectively train the model jointly over all time. Experiments demonstrate DCRL outperforms the temporal and sequential recommender models, and does capture users' short-term preferences and track the evolution of long-term preferences.
Hierarchical neural variational model for personalized sequential recommendation
Authors: Teng Xiao, Shangsong Liang, Zaiqiao Meng
Venue: Conference WWW 2019
Year: 2019

In this paper, we study the problem of recommending personalized items to users given their sequential behaviors. Most sequential recommendation models only capture a user's short-term preference in a short session, and neglect his general (unchanged over time) and long-term preferences. Besides, they are all based on deterministic neural networks, and consider users' latent preferences as point vectors in a low-dimensional continuous space. However, in real world, the evolutions of users' preferences are full of uncertainties. We address this problem by proposing a hierarchical neural variational model (HNVM). HNVM models users' three preferences: general, long-term and short-term preferences through an unified hierarchical deep generative process. HNVM is a hierarchical recurrent neural network that enables it to capture both user's long-term and short-term preferences. Experiments on two public datasets demonstrate that HNVM outperforms state-of-the-art sequential recommendation methods.
Dynamic bayesian metric learning for personalized product search
Authors: Teng Xiao, Jiaxin Ren, Zaiqiao Meng, Huan Sun, Shangsong Liang
Venue: Conference CIKM 2019
Year: 2019

In this paper, we study the problem of personalized product search under streaming scenarios. We address the problem by proposing a Dynamic Bayesian Metric Learning model, abbreviated as DBML, which can collaboratively track the evolutions of latent semantic representations of different categories of entities (i.e., users, products and words) over time in a joint metric space. In particular, unlike previous work using inner-product metric to model the affinities between entities, our DBML is a novel probabilistic metric learning approach that is able to avoid the contradicts, keep the triangle inequality in the latent space, and correctly utilize implicit feedbacks. For inferring dynamic embeddings of the entities, we propose a scalable online inference algorithm, which can jointly learn the latent representations of entities and smooth their changes across time, based on amortized inference. The inferred dynamic semantic representations of entities collaboratively inferred in a unified form by our DBML can benefit not only for improving personalized product search, but also for capturing the affinities between users, products and words. Experimental results on large datasets over a number of applications demonstrate that our DBML outperforms the state-of-the-art algorithms, and can effectively capture the evolutions of semantic representations of different categories of entities over time.
Interactive recommender system via knowledge graph-enhanced reinforcement learning
Authors: Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang 0001, Kan Ren, Ruiming Tang, Xiuqiang He 0001, Yong Yu 0001
Venue: Conference SIGIR 2020
Year: 2020

Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal with the dynamic user preference and optimize accumulative utilities, researchers have introduced reinforcement learning (RL) into IRS. However, RL methods share a common issue of sample efficiency, i.e., huge amount of interaction data is required to train an effective recommendation policy, which is caused by the sparse user responses and the large action space consisting of a large number of candidate items. Moreover, it is infeasible to collect much data with explorative policies in online environments, which will probably harm user experience. In this work, we investigate the potential of leveraging knowledge graph (KG) in dealing with these issues of RL methods for IRS, which provides rich side information for recommendation decision making. Instead of learning RL policies from scratch, we make use of the prior knowledge of the item correlation learned from KG to (i) guide the candidate selection for better candidate item retrieval, (ii) enrich the representation of items and user states, and (iii) propagate user preferences among the correlated items over KG to deal with the sparsity of user feedback. Comprehensive experiments have been conducted on two real-world datasets, which demonstrate the superiority of our approach with significant improvements against state-of-the-arts.
MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
Authors: Siddharth Bhatia, Arjit Jain, Pan Li, Ritesh Kumar, Bryan Hooi
Venue: Conference WWW 2021
Year: 2021

Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream which can detect unusual group anomalies as they occur, in a dynamic manner. MStream has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MStream is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines.
CatchSync: catching synchronized behavior in large directed graphs
Authors: Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
Venue: Conference KDD 2014
Year: 2014

Given a directed graph of millions of nodes, how can we automatically spot anomalous, suspicious nodes, judging only from their connectivity patterns? Suspicious graph patterns show up in many applications, from Twitter users who buy fake followers, manipulating the social network, to botnet members performing distributed denial of service attacks, disturbing the network traffic graph. We propose a fast and effective method, CatchSync, which exploits two of the tell-tale signs left in graphs by fraudsters: (a) synchronized behavior: suspicious nodes have extremely similar behavior pattern, because they are often required to perform some task together (such as follow the same user); and (b) rare behavior: their connectivity patterns are very different from the majority. We introduce novel measures to quantify both concepts ("synchronicity" and "normality") and we propose a parameter-free algorithm that works on the resulting synchronicity-normality plots. Thanks to careful design, CatchSync has the following desirable properties: (a) it is scalable to large datasets, being linear on the graph size; (b) it is parameter free; and (c) it is side-information-oblivious: it can operate using only the topology, without needing labeled data, nor timing information, etc., while still capable of using side information, if available. We applied CatchSync on two large, real datasets 1-billion-edge Twitter social graph and 3-billion-edge Tencent Weibo social graph, and several synthetic ones; CatchSync consistently outperforms existing competitors, both in detection accuracy by 36% on Twitter and 20% on Tencent Weibo, as well as in speed.
S-hot: Scalable high-order tucker decomposition
Authors: Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, Hwanjo Yu
Venue: Conference WSDM 2017
Year: 2017

Multi-aspect data appear frequently in many web-related applications. For example, product reviews are quadruplets of (user, product, keyword, timestamp). How can we analyze such web-scale multi-aspect data? Can we analyze them on an off-the-shelf workstation with limited amount of memory?
Quantifying long range dependence in language and user behavior to improve RNNs
Authors: Francois Belletti, Minmin Chen, Ed H. Chi
Venue: Conference KDD 2019
Year: 2019

Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data. Long Range Dependence (LRD) --- referring to long-range correlations decaying as a power law rather than exponentially w.r.t. distance --- demands a different set of tools for modeling the underlying dynamics of the sequential data. While it has been widely conjectured that LRD is present in language modeling and sequential recommendation, the amount of LRD in the corresponding sequential datasets has not yet been quantified in a scalable and model-independent manner. We propose a principled estimation procedure of LRD in sequential datasets based on established LRD theory for real-valued time series and apply it to sequences of symbols with million-item-scale dictionaries. In our measurements, the procedure estimates reliably the LRD in the behavior of users as they write Wikipedia articles and as they interact with YouTube. We further show that measuring LRD better informs modeling decisions in particular for RNNs whose ability to capture LRD is still an active area of research. The quantitative measure informs new Evolutive Recurrent Neural Networks (EvolutiveRNNs) designs, leading to state-of-the-art results on language understanding and sequential recommendation tasks at a fraction of the computational cost.
Extracting optimal performance from dynamic time warping
Authors: Abdullah Mueen, Eamonn J. Keogh
Venue: Conference KDD 2016
Year: 2016

Dynamic Time Warping (DTW) is a distance measure that compares two time series after optimally aligning them. DTW is being used for decades in thousands of academic and industrial projects despite the very expensive computational complexity, O(n2). These applications include data mining, image processing, signal processing, robotics and computer graphics among many others. In spite of all this research effort, there are many myths and misunderstanding about DTW in the literature, for example "it is too slow to be useful" or "the warping window size does not matter much." In this tutorial, we correct these misunderstandings and we summarize the research efforts in optimizing both the efficiency and effectiveness of both the basic DTW algorithm, and of the higher-level algorithms that exploit DTW such as similarity search, clustering and classification. We will discuss variants of DTW such as constrained DTW, multidimensional DTW and asynchronous DTW, and optimization techniques such as lower bounding, early abandoning, run-length encoding, bounded approximation and hardware optimization. We will discuss a multitude of application areas including physiological monitoring, social media mining, activity recognition and animal sound processing. The optimization techniques are generalizable to other domains on various data types and problems.
Crpp: Competing recurrent point process for modeling visibility dynamics in information diffusion
Authors: Avirup Saha, Bidisha Samanta, Niloy Ganguly, Abir De
Venue: Conference CIKM 2018
Year: 2018

Accurate modeling of how the visibility of a piece of information varies across time has a wide variety of applications. For example, in an e-commerce site like Amazon, it can help to identify which product is preferred over others; in Twitter, it can predict which hashtag may go viral against others. Visibility of a piece of information, therefore, indicates the ability of a piece of information to attract the attention of the users, against the rest. Therefore, apart from the individual information diffusion processes, the information visibility dynamics also involves a competition process, where each information diffusion process competes against each other to draw the attention of users. Despite models of individual information diffusion processes abounding in literature, modeling the competition process is left unaddressed. In this paper, we propose Competing Recurrent Point Process (CRPP), a probabilistic deep machinery that unifies the nonlinear generative dynamics of a collection of diffusion processes, and inter-process competition - the two ingredients of visibility dynamics. To design this model, we rely on a recurrent neural network (RNN) guided generative framework, where the recurrent unit captures the joint temporal dynamics of a group of processes. This is aided by a discriminative model which captures the underlying competition process by discriminating among the various processes using several ranking functions. On ten diverse datasets crawled from Amazon and Twitter, CRPP offers a substantial performance boost in predicting item visibility against several baselines, thereby achieving significant accuracy in predicting both the collective diffusion mechanism and the underlying competition processes.
On convexity and bounds of fairness-aware classification
Authors: Yongkai Wu, Lu Zhang, Xintao Wu
Venue: Conference WWW 2019
Year: 2019

In this paper, we study the fairness-aware classification problem by formulating it as a constrained optimization problem. Several limitations exist in previous works due to the lack of a theoretical framework for guiding the formulation. We propose a general fairness-aware framework to address previous limitations. Our framework provides: (1) various fairness metrics that can be incorporated into classic classification models as constraints; (2) the convex constrained optimization problem that can be solved efficiently; and (3) the lower and upper bounds of real-world fairness measures that are established using surrogate functions, providing a fairness guarantee for constrained classifiers. Within the framework, we propose a constraint-free criterion under which any learned classifier is guaranteed to be fair in terms of the specified fairness metric. If the constraint-free criterion fails to satisfy, we further develop the method based on the bounds for constructing fair classifiers. The experiments using real-world datasets demonstrate our theoretical results and show the effectiveness of the proposed framework.
Towards twitter context summarization with user influence models
Authors: Yi Chang, Xuanhui Wang, Qiaozhu Mei, Yan Liu
Venue: Conference WSDM 2013
Year: 2013

Twitter has become one of the most popular platforms for users to share information in real time. However, as an individual tweet is short and lacks sufficient contextual information, users cannot effectively understand or consume information on Twitter, which can either make users less engaged or even detached from using Twitter. In order to provide informative context to a Twitter user, we propose the task of Twitter context summarization, which generates a succinct summary from a large but noisy Twitter context tree. Traditional summarization techniques only consider text information, which is insufficient for Twitter context summarization task, since text information on Twitter is very sparse. Given that there are rich user interactions in Twitter, we thus study how to improve summarization methods by leveraging such signals. In particular, we study how user influence models, which project user interaction information onto a Twitter context tree, can help Twitter context summarization within a supervised learning framework. To evaluate our methods, we construct a data set by asking human editors to manually select the most informative tweets as a summary. Our experimental results based on this editorial data set show that Twitter context summarization is a promising research topic and pairwise user influence signals can significantly improve the task performance.
Graph-of-word and TW- IDF: new approach to ad hoc IR
Authors: François Rousseau, Michalis Vazirgiannis
Venue: Conference CIKM 2013
Year: 2013

In this paper, we introduce novel document representation (graph-of-word) and retrieval model (TW-IDF) for ad hoc IR. Questioning the term independence assumption behind the traditional bag-of-word model, we propose a different representation of a document that captures the relationships between the terms using an unweighted directed graph of terms. From this graph, we extract at indexing time meaningful term weights (TW) that replace traditional term frequencies (TF) and from which we define a novel scoring function, namely TW-IDF, by analogy with TF-IDF. This approach leads to a retrieval model that consistently and significantly outperforms BM25 and in some cases its extension BM25+ on various standard TREC datasets. In particular, experiments show that counting the number of different contexts in which a term occurs inside a document is more effective and relevant to search than considering an overall concave term frequency in the context of ad hoc IR.