OpenRaiser/Intern-Atlas huggingface dataset:
Web-based Startup Success Prediction
Authors: Boris Sharchilev, Michael Roizner, Andrey Yu. Rumyantsev, Denis Ozornin, Pavel Serdyukov, Maarten de Rijke
Venue: CIKM
Year: 2018

We consider the problem of predicting the success of startup companies at their early development stages. We formulate the task as predicting whether a company that has already secured initial (seed or angel) funding will attract a further round of investment in a given period of time. Previous work on this task has mostly been restricted to mining structured data sources, such as databases of the startup ecosystem consisting of investors, incubators and startups. Instead, we investigate the potential of using web-based open sources for the startup success prediction task and model the task using a very rich set of signals from such sources. In particular, we enrich structured data about the startup ecosystem with information from a business- and employment-oriented social networking service and from the web in general. Using these signals, we train a robust machine learning pipeline encompassing multiple base models using gradient boosting. We show that utilizing companies' mentions on the Web yields a substantial performance boost in comparison to only using structured data about the startup ecosystem. We also provide a thorough analysis of the obtained model that allows one to obtain insights into both the types of useful signals discoverable on the Web and market mechanisms underlying the funding process.
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
Authors: Jiaming Shen, Maryam Karimzadehgan, Michael Bendersky, Zhen Qin, Donald Metzler
Venue: CIKM
Year: 2018

User information needs vary significantly across different tasks, and therefore their queries will also differ considerably in their expressiveness and semantics. Many studies have been proposed to model such query diversity by obtaining query types and building query-dependent ranking models. These studies typically require either a labeled query dataset or clicks from multiple users aggregated over the same document. These techniques, however, are not applicable when manual query labeling is not viable, and aggregated clicks are unavailable due to the private nature of the document collection, e.g., in email search scenarios. In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models. We first develop a hierarchical clustering algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine query types. Then, we study three query-dependent ranking models, including two neural models that leverage query type information as additional features, and one novel multi-task neural model that views query type as the label for the auxiliary query cluster prediction task. This multi-task model is trained to simultaneously rank documents and predict query types. Our experiments on tens of millions of real-world email search queries demonstrate that the proposed multi-task model can significantly outperform the baseline neural ranking models, which either do not incorporate query type information or just simply feed query type as an additional feature.
Parallel Hashing Using Representative Points in Hyperoctants
Authors: Chaomin Shen, Mixue Yu, Chenxiao Zhao, Yaxin Peng, Guixu Zhang
Venue: CIKM
Year: 2018

The goal of hashing is to learn a low-dimensional binary representation of high-dimensional information, leading to a tremendous reduction of computational cost. Previous studies usually achieved this goal by applying projection or quantization methods. However, the projection method fails to capture the intrinsic data structures, and the quantization method cannot make full use of complete information by its strategy of partitioning original space. To combine their advantages and avoid their drawbacks, we propose a novel algorithm, termed as representative points quantization (RPQ), by using the representative points defined as the barycenters of points in the hyperoctants. To settle the problem of exponential time complexity with the growth of the coding length, for long hashing codes, we further propose a parallel RPQ (PRPQ) algorithm, by separating a long code into several short codes, re-coding the short codes in different low dimensional subspaces, and then concatenating them to a long code. Experiments on image retrieval tasks demonstrate that RPQ and PRPQ can well capture the main topology structure of data, showing that our algorithm achieves better performance than state-of-the-art methods.
Newsfeed Filtering and Dissemination for Behavioral Therapy on Social Network Addictions
Authors: Hong-Han Shuai, Yen-Chieh Lien, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu
Venue: CIKM
Year: 2018

While the popularity of online social network (OSN) apps continues to grow, little attention has been drawn to the increasing cases of Social Network Addictions (SNAs). In this paper, we argue that by mining OSN data in support of online intervention treatment, data scientists may assist mental healthcare professionals to alleviate the symptoms of users with SNA in early stages. Our idea, based on behavioral therapy, is to incrementally substitute highly addictive newsfeeds with safer, less addictive, and more supportive newsfeeds. To realize this idea, we propose a novel framework, called Newsfeed Substituting and Supporting System (N3S), for newsfeed filtering and dissemination in support of SNA interventions. New research challenges arise in 1) measuring the addictive degree of a newsfeed to an SNA patient, and 2) properly substituting addictive newsfeeds with safe ones based on psychological theories. To address these issues, we first propose the Additive Degree Model (ADM) to measure the addictive degrees of newsfeeds to different users. We then formulate a new optimization problem aiming to maximize the efficacy of behavioral therapy without sacrificing user preferences. Accordingly, we design a randomized algorithm with a theoretical bound. A user study with 716 Facebook users and 11 mental healthcare professionals around the world manifests that the addictive scores can be reduced by more than 30%. Moreover, experiments show that the correlation between the SNA scores and the addictive degrees quantified by the proposed model is much greater than that of state-of-the-art preference based models.
Meta-Analysis for Retrieval Experiments Involving Multiple Test Collections
Authors: Ian Soboroff
Venue: CIKM
Year: 2018

Traditional practice recommends that information retrieval experiments be run over multiple test collections, to support, if not prove, that gains in performance are likely to generalize to other collections or tasks. However, because of the pooling assumptions, evaluation scores are not directly comparable across different test collections. We present a widely-used statistical tool, \em meta-analysis, as a framework for reporting results from IR experiments using multiple test collections. We demonstrate the meta-analytical approach through two standard experiments on stemming and pseudo-relevance feedback, and compare the results to those obtained from score standardization. Meta-analysis incorporates several recent recommendations in the literature, including score standardization, reporting effect sizes rather than score differences, and avoiding a reliance on null-hypothesis statistical testing, in a unified approach. It therefore represents an important methodological improvement over using these techniques in isolation.
Neural Collaborative Ranking
Authors: Bo Song, Xin Yang, Yi Cao, Congfu Xu
Venue: CIKM
Year: 2018

Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in. With the unprecedented success of deep learning in computer vision and speech recognition, recently it has been a hot topic to bridge the gap between recommender systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and items into some shared low-dimensional latent feature space, and then employs neural nets to model the interaction between the user and item latent features to obtain state-of-the-art performance on the recommendation tasks. NeuMF assumes that the non-interacted items are inherent negative and uses negative sampling to relax this assumption. In this paper, we examine an alternative approach which does not assume that the non-interacted items are necessarily negative, just that they are less preferred than interacted items. Specifically, we develop a new classification strategy based on the widely used pairwise ranking assumption. We combine our classification strategy with the recently proposed neural collaborative filtering framework, and propose a general collaborative ranking framework called Neural Network based Collaborative Ranking (NCR). We resort to a neural network architecture to model a user's pairwise preference between items, with the belief that neural network will effectively capture the latent structure of latent factors. The experimental results on two real-world datasets show the superior performance of our models in comparison with several state-of-the-art approaches.
TGNet: Learning to Rank Nodes in Temporal Graphs
Authors: Qi Song, Bo Zong, Yinghui Wu, Lu-An Tang, Hui Zhang, Guofei Jiang, Haifeng Chen
Venue: CIKM
Year: 2018

Node ranking in temporal networks are often impacted by heterogeneous context from node content, temporal, and structural dimensions. This paper introduces TGNet , a deep learning framework for node ranking in heterogeneous temporal graphs. TGNet utilizes a variant of Recurrent Neural Network to adapt context evolution and extract context features for nodes. It incorporates a novel influence network to dynamically estimate temporal and structural influence among nodes over time. To cope with label sparsity, it integrates graph smoothness constraints as a weak form of supervision. We show that the application of TGNet is feasible for large-scale networks by developing efficient learning and inference algorithms with optimization techniques. Using real-life data, we experimentally verify the effectiveness and efficiency of TGNet techniques. We also show that TGNet yields intuitive explanations for applications such as alert detection and academic impact ranking, as verified by our case study.
Communication-Efficient Distributed Deep Metric Learning with Hybrid Synchronization
Authors: Yuxin Su, Michael R. Lyu, Irwin King
Venue: CIKM
Year: 2018

Deep metric learning is widely used in extreme classification and image retrieval because of its powerful ability to learn the semantic low-dimensional embedding of high-dimensional data. However, the heavy computational cost of mining valuable pair or triplet of training data and updating models frequently in existing deep metric learning approaches becomes a barrier to apply such methods to a large-scale real-world context in a distributed environment. Moreover, existing distributed deep learning framework is not designed for deep metric learning tasks, because it is difficult to implement a smart mining policy of valuable training data. In this paper, we introduce a novel distributed framework to speed up the training process of the deep metric learning using multiple machines. Specifically, we first design a distributed sampling method to find the hard-negative samples from a broader scope of candidate samples compared to the single-machine solution. Then, we design a hybrid communication pattern and implement a decentralized data-parallel framework to reduce the communication workload while the quality of the trained deep metric models is preserved. In experiments, we show excellent performance gain compared to a full spectrum of state-of-the-art deep metric learning models on multiple datasets in terms of image clustering and image retrieval tasks.
Randomized Bit Vector: Privacy-Preserving Encoding Mechanism
Authors: Lin Sun, Lan Zhang, Xiaojun Ye
Venue: CIKM
Year: 2018

Recently, many methods have been proposed to prevent privacy leakage in record linkage by encoding record pair data into another anonymous space. Nevertheless, they cannot perform well in some circumstances due to high computational complexities, low privacy guarantees or loss of data utility. In this paper, we propose distance-aware encoding mechanisms to compare numerical values in the anonymous space. We first embed numerical values into Hamming space by a low-computational encoding algorithm with randomized bit vector. To provide rigorous privacy guarantees, we use the random response based on differential privacy to keep global indistinguishability of original data and use Laplace noises via pufferfish mechanism to provide local indistinguishability. Besides, we provide an approach for embedding and privacy-related parameters selection to improve data utility. Experiments on datasets from different data distributions and application contexts validate that our approaches can be used efficiently in privacy-preserving record linkage tasks compared with previous works and have excellent performance even under very small privacy budgets.
On Real-time Detecting Passenger Flow Anomalies
Authors: Bo Tang, Hongyin Tang, Xinzhou Dong, Beihong Jin, Tingjian Ge
Venue: CIKM
Year: 2018

In large and medium-sized cities, detecting unusual changes of crowds of people on the streets is needed for public security, transportation management, emergency control, and terrorism prevention. As public transportation has the capability to bring a large number of people to an area in a short amount of time, real-time discovery of anomalies in passenger numbers is an effective way to detect crowd anomalies. In this paper, we devise an approach called Kochab. Kochab adopts a generative model and combines the prior knowledge about passenger flows. Hence, it can detect anomalies in the numbers of incoming and outgoing passengers within a certain time and spatial area, including anomalous events along with their durations and severities. Through well-designed inference algorithms, Kochab requires only a moderate amount of historical data to be sample data. As such, Kochab shows good performance in real time and makes prompt responses to user' s interactive analysis requests. In particular, based on the recognized anomalous events, we capture event patterns which give us hints to link to activities or status in cities. In addition, for the convenience of method evaluation and comparison, we create an open Stream Anomaly Benchmark on the basis of large-scale real-world data. This benchmark will prove useful for other researchers too. Using this benchmark, we compare Kochab with four other methods. The experimental results show that Kochab is sensitive to population flow anomalies and has superior accuracy in detecting anomalies in terms of precision, recall and the F1 score.
Towards a Quantum-Inspired Framework for Binary Classification
Authors: Prayag Tiwari, Massimo Melucci
Venue: CIKM
Year: 2018

Machine Learning models learn the relationship between input and output by examples and then apply the learned models to relate unseen input. Although ML has successfully been used in almost every field, there is always room for improvement. To this end, researchers have recently been trying to implement Quantum Mechanics(QM) in ML, since it is believed that quantum-inspired ML can enhance learning rate and effectiveness. In this paper, we address a specific task of ML and present a binary classification model inspired by the quantum detection framework. We compared the model to the state of the art. Our experimental results suggest that the use of the quantum detection framework in binary classification can improve effectiveness for a number of topics of the RCV-1 test collection and that it may still provide ways to improve effectiveness for the other topics.
The Range Skyline Query
Authors: Theodoros Tzouramanis, Eleftherios Tiakas, Apostolos N. Papadopoulos, Yannis Manolopoulos
Venue: CIKM
Year: 2018

The range skyline query retrieves the dynamic skyline for every individual query point in a range by generalizing the point-based dynamic skyline query. Its wide-ranging applications enable users to submit their preferences within an interval of 'ideally sought' values across every dimension, instead of being limited to submit their preference in relation to a single sought value. This paper considers the query as a hyper-rectangle iso-oriented towards the axes of the multi-dimensional space and proposes: (i) main-memory algorithmic strategies, which are simple to implement and (ii) secondary-memory pruning mechanisms for processing range skyline queries efficiently. The proposed approach is progressive and I/O optimal. A performance evaluation of the proposed technique demonstrates its robustness and practicability.
A Hybrid Approach for Automatic Model Recommendation
Authors: Roman Vainshtein, Asnat Greenstein-Messica, Gilad Katz, Bracha Shapira, Lior Rokach
Venue: CIKM
Year: 2018

One of the challenges of automating machine learning applications is the automatic selection of an algorithmic model for a given problem. We present AutoDi, a novel and resource-efficient approach for model selection. Our approach combines two sources of information: metafeatures extracted from the data itself and word-embedding features extracted from a large corpus of academic publications. This hybrid approach enables AutoDi to select top-performing algorithms both for widely and rarely used datasets by utilizing its two types of feature sets. We demonstrate the effectiveness of our proposed approach on a large dataset of 119 datasets and 179 classification algorithms grouped into 17 families. We show that AutoDi can reach an average of 98.8% of optimal accuracy and select the optimal classification algorithm in 49.5% of all cases.
GraphRep: Boosting Text Mining, NLP and Information Retrieval with Graphs
Authors: Michalis Vazirgiannis, Fragkiskos D. Malliaros, Giannis Nikolentzos
Venue: CIKM
Year: 2018

Graphs have been widely used as modeling tools in Natural Language Processing (NLP), Text Mining (TM) and Information Retrieval (IR). Traditionally, the unigram bag-of-words representation is applied; that way, a document is represented as a multiset of its terms, disregarding dependencies between the terms. Although several variants and extensions of this modeling approach have been proposed, the main weakness comes from the underlying term independence assumption; the order of the terms within a document is completely disregarded and any relationship between terms is not taken into account in the final task. To deal with this problem, the research community has explored various representations, and to this direction, graphs constitute a well-developed model for text representation. The goal of this tutorial is to offer a comprehensive presentation of recent methods that rely on graph-based text representations to deal with various tasks in Text Mining, NLP and IR.
Semantically-Enhanced Topic Modeling
Authors: Felipe Viegas, Washington Luiz, Christian Gomes, Amir Khatibi, Sérgio D. Canuto, Fernando Mourão, Thiago Salles, Leonardo Rocha, Marcos André Gonçalves
Venue: CIKM
Year: 2018

In this paper, we advance the state-of-the-art in topic modeling by means of the design and development of a novel (semi-formal) general topic modeling framework. The novel contributions of our solution include: (i) the introduction of new semantically-enhanced data representations for topic modeling based on pooling, and (ii) the proposal of a novel topic extraction strategy - ASToC - that solves the difficulty in representing topics in our semantically-enhanced information space. In our extensive experimentation evaluation, covering 12 datasets and 12 state-of-the-art baselines, totalizing 108 tests, we exceed (with a few ties) in almost 100 cases, with gains of more than 50% against the best baselines (achieving up to 80% against some runner-ups). We provide qualitative and quantitative statistical analyses of why our solutions work so well. Finally, we show that our method is able to improve document representation in automatic text classification.
I4TSRS: A System to Assist a Data Engineer in Time-Series Dimensionality Reduction in Industry 4.0 Scenarios
Authors: Kevin Villalobos, Borja Diez, Arantza Illarramendi, Alfredo Goñi, José Miguel Blanco
Venue: CIKM
Year: 2018

The massive captured data from industrial sensors (time-series data) that could serve as relevant indicators for predictive maintenance of equipment, fault diagnosis, etc. is generating a problem related to the considerable costs associated with their storage. In this paper we present a system called I4TSRS1, available as a Web Application, that efficiently guides a data engineer in the task of obtaining industrial time-series data reduced representations that preserve their main characteristics. Dealing with those reduced representations, data storage and transmission costs can be decreased, without limiting the future exploitation of the data in different processes. The novel contribution of the I4TSRS is that it is an intelligent system that recommends the best techniques to achieve a reduced representation of time-series captured in industrial settings. Its core element is a machine learning model that combines time-series reduction techniques with extracted features from industrial time-series. We have built the model using several heterogeneous real industrial time-series.
When Optimizer Chooses Table Scans: How to Make Them More Responsive
Authors: Lijian Wan, Tingjian Ge
Venue: CIKM
Year: 2018

Recent studies show that table scans are increasingly more common than using secondary indices. Given that the optimizer may choose table scans when the selectivity is as low as 0.5% with large data, it is important to make initial query results faster for interactive data explorations. We formulate it as a query result timeliness problem, and propose two complementary approaches. The first approach builds lightweight statistics and judiciously determines an access order to data blocks for a given query. The second approach performs adaptive microscopic tuple reordering online without relying on pre-built statistics. Our systematic experimental evaluation further verifies the efficiency and efficacy of our approaches.
Word-Driven and Context-Aware Review Modeling for Recommendation
Authors: Qianqian Wang, Si Li, Guang Chen
Venue: CIKM
Year: 2018

Recently, convolutional neural networks(CNNs) has been demonstrated to effectively model reviews in recommender systems, due to the learning of contextual features such as surrounding words and word order for reviews. However, CNNs with max-pooling fails to capture the count information of contextual features, since the feature map generated by a convolution filter can only get a max feature value with max-pooling. If the max feature value appears more than once in the feature map, CNNs will lose the count information of the contextual feature. The count information is quite critical for modeling reviews, for example, ten "a good quality" is more credible than one "a good quality" for representing item properties, and five "the scenery is" shows that a user may pay more attention to the scenery than one "the scenery is" does. Our model, called WCN-MF, extends CNNs by introducing a new module named Deep Latent Dirichlet Allocation (DLDA) to capture the count information of contextual features. DLDA is inspired by the fact that contextual features consist of words, hence, we can first capture the count information of words, and second generate contextual features with words. By combining DLDA with CNNs, we can get a word-driven and context-aware review representation. Further, we incorporate the review representation with Matrix Factorization for recommendation. Our evaluations on three real- world datasets reveal that our model can significantly outperform the state-of-the-art recommendation models.