OpenRaiser/Intern-Atlas huggingface dataset:
Deep Graph Embedding for Ranking Optimization in E-commerce
Authors: Chen Chu, Zhao Li, Beibei Xin, Fengchao Peng, Chuanren Liu, Remo Rohs, Qiong Luo, Jingren Zhou
Venue: CIKM
Year: 2018

Matching buyers with most suitable sellers providing relevant items (e.g., products) is essential for e-commerce platforms to guarantee customer experience. This matching process is usually achieved through modeling inter-group (buyer-seller) proximity by e-commerce ranking systems. However, current ranking systems often match buyers with sellers of various qualities, and the mismatch is detrimental to not only buyers' level of satisfaction but also the platforms' return on investment (ROI). In this paper, we address this problem by incorporating intra-group structural information (e.g., buyer-buyer proximity implied by buyer attributes) into the ranking systems. Specifically, we propose De ep Gr aph E mbe dding (DEGREE), a deep learning based method, to exploit both inter-group and intra-group proximities jointly for structural learning. With a sparse filtering technique, DEGREE can significantly improve the matching performance with computation resources less than that of alternative deep learning based methods. Experimental results demonstrate that DEGREE outperforms state-of-the-art graph embedding methods on real-world e-commence datasets. In particular, our solution boosts the average unit price in purchases during an online A/B test by up to 11.93%, leading to better operational efficiency and shopping experience.
FA + TA <FSA: Flexible Score Aggregation
Authors: Paolo Ciaccia, Davide Martinenghi
Venue: CIKM
Year: 2018

The problem of aggregating scores, so as to provide a ranking of objects in a dataset according to different evaluation criteria, is central to many modern data-intensive applications. Although efficient (instance optimal) algorithms exist to this purpose (such as the Threshold Algorithm TA and its variants) none of them is able to deal with scenarios in which the function used to aggregate scores is only partially specified. This is the typical case when the function is a weighted sum, and the user is unable to provide precise values for the weights. In this paper, we consider the problem of processing multi-source top-k queries, when only constraints, rather than precise values, are available for the weights. After observing that the so-called Fagin's Algorithm (FA) can be adapted to solve the problem, yet only when no constraints at all are present (a case in which our queries will return the k-skyband of the dataset), we introduce the novel FSA algorithm, which we prove to be instance optimal for any set of constraints on the weights. We also propose several optimizations to the basic FSA logic so as to improve execution times. Experimental analysis on both real and synthetic datasets shows that our optimizations are indeed highly effective and that the increased flexibility provided by FSA introduces little overhead with respect to the case of classical top-k queries.
CIKM 2018 Co-Located Workshops Summary
Authors: Alfredo Cuzzocrea, Francesco Bonchi, Dimitrios Gunopulos
Venue: CIKM
Year: 2018

This paper provides an overview of the workshops co-located with the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), held during October 22-26, 2018 in Turin, Italy.
ASTRO: A Datalog System for Advanced Stream Reasoning
Authors: Ariyam Das, Sahil M. Gandhi, Carlo Zaniolo
Venue: CIKM
Year: 2018

The rise of the Internet of Things (IoT) and the recent focus on a gamut of 'Smart City' initiatives world-wide have pushed for new advances in data stream systems to (1) support complex analytics and evolving graph applications as continuous queries, and (2) deliver fast and scalable processing on large data streams. Unfortunately current continuous query languages (CQL) lack the features and constructs needed to support the more advanced applications. For example recursive queries are now part of SQL, Datalog, and other query languages, but they are not supported by most CQLs, a fact that caused a significant loss of expressive power, which is further aggravated by the limitation that only non-blocking queries can be supported. To overcome these limitations we have developed an a dvanced st ream r easo ning system ASTRO that builds on recent advances in supporting aggregates in recursive queries. In this demo, we will briefly elucidate the formal Streamlog semantics, which combined with the Pre-Mappability (PreM) concept, allows the declarative specification of many complex continuous queries, which are then efficiently executed in real-time by the portable ASTRO architecture. Using different case studies, we demonstrate (i) the ease-of-use, (ii) the expressive power and (iii) the robustness of our system, as compared to other state-of-the-art declarative CQL systems.
Signed Network Modeling Based on Structural Balance Theory
Authors: Tyler Derr, Charu C. Aggarwal, Jiliang Tang
Venue: CIKM
Year: 2018

The modeling of networks, specifically generative models, has been shown to provide a plethora of information about the underlying network structures, as well as many other benefits behind their construction. There has been a considerable increase in interest for the better understanding and modeling of networks, and the vast majority of existing work has been for unsigned networks. However, many networks can have positive and negative links (or signed networks), especially in online social media. It is evident from recent work that signed networks present unique properties and principles from unsigned networks due to the added complexity, which pose tremendous challenges on existing unsigned network models. Hence, in this paper, we investigate the problem of modeling signed networks. In particular, we provide a principled approach to capture important properties and principles of signed networks and propose a novel signed network model guided by Structural Balance Theory. Empirical experiments on three real-world signed networks demonstrate the effectiveness of the proposed model.
CEC: Constraints based Explanation for Classifications
Authors: Daniel Deutch, Nave Frost
Venue: CIKM
Year: 2018

Explaining the results of data-intensive computation via provenance has been extensively studied in the literature. We focus here on explaining the output of Machine Learning Classifiers, which are main components of many contemporary Data Science applications. We have developed a simple generic approach for explaining classification results, by looking for constrained perturbations to parts of the input that would have the most significant effect on the classification. Our solution requires white-box access to the model internals and a specification of constraints that define which perturbations are "reasonable" in the application domain; both are typically available to the data scientist. We propose to demonstrate CEC, a system prototype that is based on these foundations to provide generic explanations for Neural Networks and Random Forests. We will demonstrate the system usefulness in the context of two application domains: bank marketing campaigns, and visually clear explanations for image classifications. We will highlight the benefit that such explanations could yield to the data scientist and interactively engage the audience in computing and viewing explanations for different cases and different sets of constraints.
Preserving Privacy of Fraud Detection Rule Sharing Using Intel's SGX
Authors: Daniel Deutch, Yehonatan Ginzberg, Tova Milo
Venue: CIKM
Year: 2018

The collaboration of financial institutes against fraudsters is a promising path for reducing resource investments and increasing coverage. Yet, such collaboration is held back by two somewhat conflicting challenges: effective knowledge sharing and limiting leakage of private information. While the censorship of private information is likely to reduce knowledge sharing effectiveness, the generalization of private information to a desired degree can potentially allow, on one hand, to limit the leakage, and on the other hand, to reveal some properties of the private information that can be beneficial for sharing. In this demo we present a system that allows knowledge sharing via effective adaptation of fraud detection rules while preserving privacy. The system uses taxonomies to generalize concrete values appearing in fraud detection rules to higher level concepts which conform to some privacy/utility requirements set by the owner. Our demonstration will engage the CIKM'18 audience by showing that private information can be abstracted to enforce privacy while maintaining its usage by (partially) trusted allies.
Optimizing Boiler Control in Real-Time with Machine Learning for Sustainability
Authors: Yukun Ding, Jinglan Liu, Jinjun Xiong, Meng Jiang, Yiyu Shi
Venue: CIKM
Year: 2018

In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability. In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of temperature in different zones and oxygen content from the flue to improve the boiler's stability and energy efficiency. We employ an efficient algorithm by integrating appropriate machine learning and optimization techniques. We obtain a large dataset collected from a real boiler for more than two months from our industry partner, and conduct extensive experiments to demonstrate the effectiveness and efficiency of the proposed algorithm.
Explicit Preference Elicitation for Task Completion Time
Authors: Mohammadreza Esfandiari, Senjuti Basu Roy, Sihem Amer-Yahia
Venue: CIKM
Year: 2018

Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing platforms rely heavily on observing workers and inferring their preferences implicitly. On the contrary, we believe that asking workers to indicate their preferences explicitly will allow us to improve different processes in crowdsourcing platforms. We initiate a study that leverages explicit elicitation from workers to capture the evolving nature of worker preferences and we propose an optimization framework to better understand and estimate task completion time. We design a Worker model to estimate task completion time whose accuracy is improved iteratively by requesting worker preferences for task factors, such as, required skills, task payment, and task relevance. We develop efficient solutions with guarantees, run extensive experiments with large-scale real-world data that show the benefit of explicit preference elicitation over implicit ones with statistical significance.
A Globalization-Semantic Matching Neural Network for Paraphrase Identification
Authors: Miao Fan, Wutao Lin, Yue Feng, Mingming Sun, Ping Li
Venue: CIKM
Year: 2018

Paraphrase identification (PI) aims at determining whether two natural language sentences roughly have identical meaning. PI has been conventionally formalized as a binary classification task and widely used in many talks such as text summarization, plagiarism detection, etc. The emergence of deep neural networks (DNNs) renovates and dominates the learning paradigm of PI, as DNNs do not rely on lexical nor syntactic knowledge of a language, unlike traditional methods. State-of-the-art DNNs-based approaches to PI mainly adopt multi-layer convolutional neural networks (CNNs) to model paraphrastic sentences, which could discover alignments of phrases with the same length (unigram-to-unigram, bigram-to-bigram, trigram-to-trigram, etc.) at each layer. However, paraphrasing phenomena globally exist at all levels of granularity between a pair of paraphrastic sentences, i.e., word-to-word, word-to-phrase, phrase-to-phrase, and even sentence-to-sentence. In this paper, we contribute a globalization-semantic matching neural network (GSMNN) paradigm which has been deployed in Baidu.com to tackle practical PI problems. Established on a weight-sharing single-layer CNN, GSMNN is composed of a multi-granular matching layer with the attention mechanism and a sentence-level matching layer. These layers are designed to capture essentially all phenomena of semantic matching. Evaluations are conducted on a public large-scale dataset for PI: Quora-QP which contains more than 400,000 paraphrasing and non-paraphrasing question pairs from Quora.com. Experimental results show that GSMNN outperforms the state-of-the-art model by a substantial margin.
Abnormal Event Detection via Heterogeneous Information Network Embedding
Authors: Shaohua Fan, Chuan Shi, Xiao Wang
Venue: CIKM
Year: 2018

Heteregeneous information networks (HINs) are ubiquitous in the real world, and discovering the abnormal events plays an important role in understanding and analyzing the HIN. The abnormal event usually implies that the number of co-occurrences of entities in a HIN are very rare, so most of the existing works are based on detecting the rare patterns of events. However, we find that the number of co-occurrences of majority entities in events are the same, which brings great challenge to distinguish the normal and abnormal events. Therefore, we argue that considering the heterogeneous information structure only is not sufficient for abnormal event detection and introducing additional valuable information is necessary. In this paper, we propose a novel deep heterogeneous network embedding method which incorporates the entity attributes and second-order structures simultaneously to address this problem. Specifically, we utilize type-aware Multilayer Perceptron (MLP) component to learn the attribute embedding, and adopt the autoencoder framework to learn the second-order aware embedding. Then based on the mixed embeddings, we are able to model the pairwise interactions of different entities, such that the events with small entity compatibilities have large abnormal event score. The experimental results on real world network demonstrate the effectiveness of our proposed method.
TED-KISS: A Known-Item Speech Video Search Benchmark
Authors: Fan Fang, Bo-Wen Zhang, Xu-Cheng Yin, Haixia Man, Fang Zhou
Venue: CIKM
Year: 2018

Known-item search is an everyday natural scenario that we search for a specific thing (maybe a song) while only remembering some details about it. Existing benchmarks generally focus on brief user requests which specify some metadata like the title, or the time. However, in most cases, the users can hardly recall such information accurately. In order to embrace the research of known-item search, we present a new publicly available known-item speech video search benchmark, namely TED-KISS, which takes TED talks as an example. The video collection is constructed with up-to-date nearly 80,000 TED and TEDx talks on Youtube. These talks cover various topics, and their titles, speakers, descriptions, full-text subtitles, as well as original links are extracted as metadata, which makes the researches on text-based retrieval and multimedia retrieval feasible. Unlike other benchmarks concerning visual contents in segments, the user requests in TED-KISS are generated through a more natural process, partly through original related topics posted on Reddit and Baidu Tieba, and partly through manual imitative requests annotated by volunteers in a scenario simulation. In addition, we analyze the characteristics of our benchmark through evaluations of several existing text-based IR and Neural-IR models, which also can be served as baselines for this task.
Continuation Methods and Curriculum Learning for Learning to Rank
Authors: Nicola Ferro, Claudio Lucchese, Maria Maistro, Raffaele Perego
Venue: CIKM
Year: 2018

In this paper we explore the use of Continuation Methods and Curriculum Learning techniques in the area of Learning to Rank. The basic idea is to design the training process as a learning path across increasingly complex training instances and objective functions. We propose to instantiate continuation methods in Learning to Rank by changing the IR measure to optimize during training, and we present two different curriculum learning strategies to identify easy training examples. Experimental results show that simple continuation methods are more promising than curriculum learning ones since they allow for slightly improving the performance of state-of-the-art λ-MART models and provide a faster convergence speed.
Data Structure for Efficient Line of Sight Queries
Authors: Swapnil Gaikwad, Melody Moh, David C. Anastasiu
Venue: CIKM
Year: 2018

Given the great amounts of data being transmitted between devices in the 21st century, existing channels of wireless communication are getting congested. In the wireless space, the focus up to now has been on the microwave frequency range. An alternative for high-speed medium- and long-range communication is the millimeter wave spectrum, which is most effectively used through point-to-point links. In this paper, we develop and compare methods for verifying the Line of Sight (LOS) constraint between two points in a city. To be useful for online wireless network planning systems, the methods must be able to process terabytes of 3D city geolocation data and provide answers in milliseconds. We evaluate our methods using data for the city of San Jose, a major metropolitan area in Silicon Valley, California. Our results indicate that our Hierarchical Polygon Aggregation (HPA) method is able to achieve millisecond-level query times with very little loss of precision.
User Identification with Spatio-Temporal Awareness across Social Networks
Authors: Xing Gao, Wenli Ji, Yongjun Li, Yao Deng, Wei Dong
Venue: CIKM
Year: 2018

User Identification with Spatio-Temporal awareness has been attracting much attention from academia. The existing methods not only handle temporal and spatial data separately but also do not consider conflictive check-in records. To tackle these problems, we propose a novel approach that consists of three parts. 1) Measure the similarity of users with a kernel density estimation based method, which handles the spatial and temporal data together; 2) Assign weights to check-in records following the idea of TFIDF, which highlights the discriminative check-in records; 3) Penalize the similarity of users based on the number of conflictive check-in records. The pair-wise users with similarity higher than a predefined threshold are considered to belong to the same individual. Experiments demonstrate the superiority of the proposed approach.
"Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention
Authors: Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit P. Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Jyotishman Pathak
Venue: CIKM
Year: 2018

Social media platforms are increasingly being used to share and seek advice on mental health issues. In particular, Reddit users freely discuss such issues on various subreddits, whose structure and content can be leveraged to formally interpret and relate subreddits and their posts in terms of mental health diagnostic categories. There is prior research on the extraction of mental health-related information, including symptoms, diagnosis, and treatments from social media; however, our approach can additionally provide actionable information to clinicians about the mental health of a patient in diagnostic terms for web-based intervention. Specifically, we provide a detailed analysis of the nature of subreddit content from domain expert's perspective and introduce a novel approach to map each subreddit to the best matching DSM-5 (Diagnostic and Statistical Manual of Mental Disorders - 5th Edition) category using multi-class classifier. Our classification algorithm analyzes all the posts of a subreddit by adapting topic modeling and word-embedding techniques, and utilizing curated medical knowledge bases to quantify relationship to DSM-5 categories. Our semantic encoding-decoding optimization approach reduces the false-alarm-rate from 30% to 2.5% over a comparable heuristic baseline, and our mapping results have been verified by domain experts achieving a kappa score of 0.84.
A Content-Based Approach for Modeling Analytics Operators
Authors: Ioannis Giannakopoulos, Dimitrios Tsoumakos, Nectarios Koziris
Venue: CIKM
Year: 2018

The plethora of publicly available data sources has given birth to a wealth of new needs and opportunities. The ever increasing amount of data has shifted the analysts' attention from optimizing the operators for specific business cases, to focusing on datasets per se, selecting the ones that are most suitable for specific operators, i.e., they make an operator produce a specific output. Yet, predicting the output of a given operator executed for different input datasets is not an easy task: It entails executing the operator for all of them, something that requires excessive computational power and time. To tackle this challenge, we propose a novel dataset profiling methodology that infers an operator's outcome based on examining the similarity of the available input datasets in specific attributes. Our methodology quantifies dataset similarities and projects them into a low-dimensional space. The operator is then executed for a mere subset of the available datasets and its output for the rest of them is approximated using Neural Networks trained using this space as input. Our experimental evaluation thoroughly examines the performance of our scheme using both synthetic and real-world datasets, indicating that the suggested approach is capable of predicting an operator's output with high accuracy. Moreover, it massively accelerates operator profiling in comparison to approaches that require an exhaustive operator execution, rendering our work ideal for cases where a multitude of operators need to be executed to a set of given datasets.
Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization
Authors: Yongshun Gong, Zhibin Li, Jian Zhang, Wei Liu, Yu Zheng, Christina Kirsch
Venue: CIKM
Year: 2018

Crowd Flow Prediction (CFP) is one major challenge in the intelligent transportation systems of the Sydney Trains Network. However, most advanced CFP methods only focus on entrance and exit flows at the major stations or a few subway lines, neglecting Crowd Flow Distribution (CFD) forecasting problem across the entire city network. CFD prediction plays an irreplaceable role in metro management as a tool that can help authorities plan route schedules and avoid congestion. In this paper, we propose three online non-negative matrix factorization (ONMF) models. ONMF-AO incorporates an Average Optimization strategy that adapts to stable passenger flows. ONMF-MR captures the Most Recent trends to achieve better performance when sudden changes in crowd flow occur. The Hybrid model, ONMF-H, integrates both ONMF-AO and ONMF-MR to exploit the strengths of each model in different scenarios and enhance the models' applicability to real-world situations. Given a series of CFD snapshots, both models learn the latent attributes of the train stations and, therefore, are able to capture transition patterns from one timestamp to the next by combining historic guidance. Intensive experiments on a large-scale, real-world dataset containing transactional data demonstrate the superiority of our ONMF models.