OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic Skyline on Incomplete Data
Authors: Kaiqi Zhang, Hong Gao, Xixian Han, Zhipeng Cai, Jianzhong Li
Venue: CIKM
Year: 2017

The skyline query is important in database community. In recent years, the researches on incomplete data have been increasingly considered, especially for the skyline query. However, the existing skyline definition on incomplete data cannot provide users with valuable references. In this paper, we propose a novel skyline definition utilizing probabilistic model on incomplete data where each point has a probability to be in the skyline. In particular, it returnsK points with the highest skyline probabilities. Meanwhile, it is a big challenge to compute probabilistic skyline on incomplete data. We propose an efficient algorithm PISkyline, which utilizes two pruning strategies to reduce the number of points and adopts two optimizations to accelerate probability computation for each point. Nevertheless, PISkyline is susceptible to the order of input data and there is still a great deal of room for optimization. We develop a point-level sorting technique by adjusting the order of accessing points to further improve the efficiency of PISkyline. Our experimental results demonstrate that our algorithms are tens of times faster than the naive algorithm on both synthetic and real datasets.
Name Disambiguation in Anonymized Graphs using Network Embedding
Authors: Baichuan Zhang, Mohammad Al Hasan
Venue: CIKM
Year: 2017

In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly better than the existing name disambiguation methods working in a similar setting.
Temporal Analog Retrieval using Transformation over Dual Hierarchical Structures
Authors: Yating Zhang, Adam Jatowt, Katsumi Tanaka
Venue: CIKM
Year: 2017

In recent years, we have witnessed a rapid increase of text con- tent stored in digital archives such as newspaper archives or web archives. Many old documents have been converted to digital form and made accessible online. Due to the passage of time, it is however difficult to effectively perform search within such collections. Users, especially younger ones, may have problems in finding appropriate keywords to perform effective search due to the terminology gap arising between their knowledge and the unfamiliar domain of archival collections. In this paper, we provide a general framework to bridge different domains across-time and, by this, to facilitate search and comparison as if carried in user's familiar domain (i.e., the present). In particular, we propose to find analogical terms across temporal text collections by applying a series of transformation procedures. We develop a cluster-biased transformation technique which makes use of hierarchical cluster structures built on the temporally distributed document collections. Our methods do not need any specially prepared training data and can be applied to diverse collections and time periods. We test the performance of the proposed approaches on the collections separated by both short (e.g., 20 years) and long time gaps (70 years), and we report improvements in range of 18%-27% over short and 56%-92% over long periods when compared to state-of-the-art baselines.
Learning Node Embeddings in Interaction Graphs
Authors: Yao Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu
Venue: CIKM
Year: 2017

Node embedding techniques have gained prominence since they produce continuous and low-dimensional features, which are effective for various tasks. Most existing approaches learn node embeddings by exploring the structure of networks and are mainly focused on static non-attributed graphs. However, many real-world applications, such as stock markets and public review websites, involve bipartite graphs with dynamic and attributed edges, called attributed interaction graphs. Different from conventional graph data, attributed interaction graphs involve two kinds of entities (e.g. investors/stocks and users/businesses) and edges of temporal interactions with attributes (e.g. transactions and reviews). In this paper, we study the problem of node embedding in attributed interaction graphs. Learning embeddings in interaction graphs is highly challenging due to the dynamics and heterogeneous attributes of edges. Different from conventional static graphs, in attributed interaction graphs, each edge can have totally different meanings when the interaction is at different times or associated with different attributes. We propose a deep node embedding method called IGE (Interaction Graph Embedding). IGE is composed of three neural networks: an encoding network is proposed to transform attributes into a fixed-length vector to deal with the heterogeneity of attributes; then encoded attribute vectors interact with nodes multiplicatively in two coupled prediction networks that investigate the temporal dependency by treating incident edges of a node as the analogy of a sentence in word embedding methods. The encoding network can be specifically designed for different datasets as long as it is differentiable, in which case it can be trained together with prediction networks by back-propagation. We evaluate our proposed method and various comparing methods on four real-world datasets. The experimental results prove the effectiveness of the learned embeddings by IGE on both node clustering and classification tasks.
Predicting Startup Crowdfunding Success through Longitudinal Social Engagement Analysis
Authors: Qizhen Zhang, Tengyuan Ye, Meryem Essaidi, Shivani Agarwal, Vincent Liu, Boon Thau Loo
Venue: CIKM
Year: 2017

A key ingredient to a startup's success is its ability to raise funding at an early stage. Crowdfunding has emerged as an exciting new mechanism for connecting startups with potentially thousands of investors. Nonetheless, little is known about its effectiveness, nor the strategies that entrepreneurs should adopt in order to maximize their rate of success. In this paper, we perform a longitudinal data collection and analysis of AngelList - a popular crowdfunding social platform for connecting investors and entrepreneurs. Over a 7-10 month period, we track companies that are actively fund-raising on AngelList, and record their level of social engagement on AngelList, Twitter, and Facebook. Through a series of measures on social en- gagement (e.g. number of tweets, posts, new followers), our analysis shows that active engagement on social media is highly correlated to crowdfunding success. In some cases, the engagement level is an order of magnitude higher for successful companies. We further apply a range of machine learning techniques (e.g. decision tree, SVM, KNN, etc) to predict the ability of a company to success- fully raise funding based on its social engagement and other metrics. Since fund-raising is a rare event, we explore various techniques to deal with class imbalance issues. We observe that some metrics (e.g. AngelList followers and Facebook posts) are more signi cant than other metrics in predicting fund-raising success. Furthermore, despite the class imbalance, we are able to predict crowdfunding success with 84% accuracy.
Communication-Efficient Distributed Skyline Computation
Authors: Haoyu Zhang, Qin Zhang
Venue: CIKM
Year: 2017

In this paper we study skyline queries in the distributed computational model, where we have s remote sites and a central coordinator; each site holds a piece of data, and the coordinator wants to compute the skyline of the union of the s datasets. The computation is in terms of rounds, and the goal is to minimize both the total communication cost and the round cost.
Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data
Authors: Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu, Naren Ramakrishnan
Venue: CIKM
Year: 2017

Hyper-local pricing data, e.g., about foods and commodities, exhibit subtle spatiotemporal variations that can be useful as crucial precursors of future events. Three major challenges in modeling such pricing data include: i) temporal dependencies underlying features; ii) spatiotemporal missing values; and iii) constraints underlying economic phenomena. These challenges hinder traditional event forecasting models from being applied effectively. This paper proposes a novel spatiotemporal event forecasting model that concurrently addresses the above challenges. Specifically, given continuous price data, a new soft time-lagged model is designed to select temporally dependent features. To handle missing values, we propose a data tensor completion method based on price domain knowledge. The parameters of the new model are optimized using a novel algorithm based on the Alternative Direction Methods of Multipliers (ADMM). Extensive experimental evaluations on multiple datasets demonstrate the effectiveness of our proposed approach.
Destination-aware Task Assignment in Spatial Crowdsourcing
Authors: Yan Zhao, Yang Li, Yu Wang, Han Su, Kai Zheng
Venue: CIKM
Year: 2017

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. Our empirical studies demonstrate that our proposed technique is quite effective and settle the problem nicely.
Missing Value Learning
Authors: Zhi-Lin Zhao, Chang-Dong Wang, Kun-Yu Lin, Jian-Huang Lai
Venue: CIKM
Year: 2017

Missing value is common in many machine learning problems and much effort has been made to handle missing data to improve the performance of the learned model. Sometimes, our task is not to train a model using those unlabeled/labeled data with missing value but process examples according to the values of some specified features. So, there is an urgent need of developing a method to predict those missing values. In this paper, we focus on learning from the known values to learn missing value as close as possible to the true one. It's difficult for us to predict missing value because we do not know the structure of the data matrix and some missing values may relate to some other missing values. We solve the problem by recovering the complete data matrix under the three reasonable constraints: feature relationship, upper recovery error bound and class relationship. The proposed algorithm can deal with both unlabeled and labeled data and generative adversarial idea will be used in labeled data to transfer knowledge. Extensive experiments have been conducted to show the effectiveness of the proposed algorithms.
Semi-Supervised Event-related Tweet Identification with Dynamic Keyword Generation
Authors: Xin Zheng, Aixin Sun, Sibo Wang, Jialong Han
Venue: CIKM
Year: 2017

Twitter provides us a convenient channel to get access to the immediate information about major events. However, it is challenging to acquire a clean and complete set of event-related data due to the characteristics of tweets, eg short and noisy. In this paper, we propose a semi-supervised method to obtain high quality event-related tweets from Twitter stream, in terms of precision and recall. Specifically, candidate event-related tweets are selected based on a set of keywords. We propose to generate and update these keywords dynamically along the event development. To be included in this keyword set, words are evaluated based on single word properties, property based on co-occurred words, and changes of word importance over time. Our solution is capable of capturing keywords of emerging aspects or aspects with increasing importance along event evolvement. By leveraging keyword importance information and a few labeled tweets, we propose a semi-supervised expectation maximization process to identify event-related tweets. This process significantly reduces human effort in acquiring high quality tweets. Experiments on three real world datasets show that our solution outperforms state-of-the-art approaches by up to 10% in F1 measure.
Sequence Modeling with Hierarchical Deep Generative Models with Dual Memory
Authors: Yanan Zheng, Lijie Wen, Jianmin Wang, Jun Yan, Lei Ji
Venue: CIKM
Year: 2017

Deep Generative Models (DGMs) are able to extract high-level representations from massive unlabeled data and are explainable from a probabilistic perspective. Such characteristics favor sequence modeling tasks. However, it still remains a huge challenge to model sequences with DGMs. Unlike real-valued data that can be directly fed into models, sequence data consist of discrete elements and require being transformed into certain representations first. This leads to the following two challenges. First, high-level features are sensitive to small variations of inputs as well as the way of representing data. Second, the models are more likely to lose long-term information during multiple transformations. In this paper, we propose a Hierarchical Deep Generative Model With Dual Memory to address the two challenges. Furthermore, we provide a method to efficiently perform inference and learning on the model. The proposed model extends basic DGMs with an improved hierarchically organized multi-layer architecture. Besides, our model incorporates memories along dual directions, respectively denoted as broad memory and deep memory. The model is trained end-to-end by optimizing a variational lower bound on data log-likelihood using the improved stochastic variational method. We perform experiments on several tasks with various datasets and obtain excellent results. The results of language modeling show our method significantly outperforms state-of-the-art results in terms of generative performance. Extended experiments including document modeling and sentiment analysis, prove the high-effectiveness of dual memory mechanism and latent representations. Text random generation provides a straightforward perception for advantages of our model.
Local Ensemble across Multiple Sources for Collaborative Filtering
Authors: Jing Zheng, Fuzhen Zhuang, Chuan Shi
Venue: CIKM
Year: 2017

Recently, Transfer Collaborative Filtering (TCF) methods across multiple source domains, which employ knowledge from different source domains to improve the recommendation performance in the target domain, have been applied in recommender systems. The existing multi-source TCF methods either require overlapping objects in different domains or simply re-weight domains to merge them together. In this paper, we propose a novel LO cal EN semble framework across multiple source domains for collaborative filtering (called LOEN for short), where weights of multiple sources for each missing rating in the target domain are determined according to their corresponding local structures. Compared with the previous TCF methods, LOEN does not require overlapping data and considers the divergence of sources through exploiting the local structures of ratings, which allows LOEN to be more general and effective. Experiments conducted on real datasets validate the effectiveness of LOEN, especially for knowledge transfer across unrelated source domains.
POOLSIDE: An Online Probabilistic Knowledge Base for Shopping Decision Support
Authors: Ping Zhong, Zhanhuai Li, Qun Chen, Yanyan Wang, Lianping Wang, Murtadha H. M. Ahmed, Fengfeng Fan
Venue: CIKM
Year: 2017

We present POOLSIDE, an online PrObabilistic knOwLedge base for ShoppIng DEcision support, that provides with the on-target recommendation service based on explicit user requirement. With a natural language interface, POOLSIDE can answer question in real-time. We present how to construct the knowledge base and how to enable real-time response in POOLSIDE. Finally, we demonstrate that Poolside can give high-quality product recommendations with high efficiency.(The demo video can be accessed via the link:https://www.youtube.com/watch?v=D8ALi11CUcc)
Knowledge-based Question Answering by Jointly Generating, Copying and Paraphrasing
Authors: Shuguang Zhu, Xiang Cheng, Sen Su, Shuang Lang
Venue: CIKM
Year: 2017

With the development of large-scale knowledge bases, people are building systems which give simple answers to questions based on consolidate facts. In this paper, we focus on simple questions, which ask about only a subject and relation in the knowledge base. Observing that certain parts of a question usually overlap with names of its corresponding subject and relation in the knowledge base, we argue that a question is formed by a mixture of copying and generation. To model that, we propose a sequence-to-sequence (seq2seq) architecture which encodes a candidate subject-relation pair and decodes it into the given question, where the decoding probability is used to select the best candidate. In our decoder, the copying mode points the subject or relation and duplicates its name, while the generating mode summarizes the meaning of the subject-relation pair and produces a word to smooth the question. Realizing that although sometimes a subject or relation is pointed, different names or keywords might be used, we also incorporate a paraphrasing mode to supplement the copying mode using an automatically mined lexicon. Extensive experiments on the largest dataset exhibit our better performance compared with the state-of-the-art methods.
Outlier Detection in Sparse Data with Factorization Machines
Authors: Mengxiao Zhu, Charu C. Aggarwal, Shuai Ma, Hui Zhang, Jinpeng Huai
Venue: CIKM
Year: 2017

In sparse data, a large fraction of the entries take on zero values. Some examples of sparse data include short text snippets (such as tweets in Twitter) or some feature representations of categorical data sets with a large number of values, in which traditional methods for outlier detection typically fail because of the difficulty of computing distances. To address this, it is important to use the latent relations between such values. Factorization machines represent a natural methodology for this, and are naturally designed for the massive-domain setting because of their emphasis on sparse data sets. In this study, we propose an outlier detection approach for sparse data with factorization machines. Factorization machines are also efficient due to their linear complexity in the number of non-zero values. In fact, because of their efficiency, they can even be extended to traditional settings for numerical data by an appropriate feature engineering effort. We show that our approach is both effective and efficient for sparse categorical, short text and numerical data by an extensive experimental study.
Cluster-level Emotion Pattern Matching for Cross-Domain Social Emotion Classification
Authors: Endong Zhu, Yanghui Rao, Haoran Xie, Yuwei Liu, Jian Yin, Fu Lee Wang
Venue: CIKM
Year: 2017

This paper addresses the task of cross-domain social emotion classification of online documents. The cross-domain task is formulated as using abundant labeled documents from a source domain and a small amount of labeled documents from a target domain, to predict the emotion of unlabeled documents in the target domain. Although several cross-domain emotion classification algorithms have been proposed, they require that feature distributions of different domains share a sufficient overlapping, which is hard to meet in practical applications. This paper proposes a novel framework, which uses the emotion distribution of training documents at the cluster level, to alleviate the aforementioned issue. Experimental results on two datasets show the effectiveness of our proposed model on cross-domain social emotion classification.
Understanding Engagement through Search Behaviour
Authors: Mengdie Zhuang, Gianluca Demartini, Elaine G. Toms
Venue: CIKM
Year: 2017

Evaluating user engagement with search is a critical aspect of understanding how to assess and improve information retrieval systems. While standard techniques for measuring user engagement use questionnaires, these are obtrusive to user interaction, and can only be collected at acceptable intervals. The problem we address is whether there is a less obtrusive and more automatic way to assess how users perceive the search process and outcome. Log files collect behavioural signals (e.g., clicks, queries) from users on a large scale. In this paper, we investigate the potential to predict how users perceive engagement with search by modelling behavioural signals from log files using supervised learning methods. We focus on different engagement dimensions (Perceived Usability, Felt Involvement, Endurability and Novelty) and examine how 37 behavioural features can inform these dimensions. Our results, obtained from 377 in-lab participants undergoing goal-based search tasks, support the connection between perceived engagement and search behaviour. More specifically, we show that time- and query-related features are best suited for predicting user perceived engagement, and suggest that different behavioural features better reflect specific dimensions. We demonstrate the possibility of predicting user-perceived engagement using search behavioural features.