OpenRaiser/Intern-Atlas huggingface dataset:
GLUE: a Parameter-Tuning-Free Map Updating System
Authors: Hao Wu, Chuanchuan Tu, Weiwei Sun, Baihua Zheng, Hao Su, Wei Wang
Venue: CIKM
Year: 2015

Map data are widely used in mobile services, but most maps might not be complete. Updating the map automatically is an important problem because road networks are frequently changed with the development of the city. This paper studies the problem of recovering missing road segments via GPS trajectories, especially low sampled data. Our approach takes the GPS noise into consideration and proposes an effective self-adaptive algorithm. Besides, we propose theoretical models behind all the important parameters to enable self-adaptive parameter setting. To the best of our knowledge, this is the first work that addresses the parameter setting issue successfully to make sure our approach is free of parameter-tuning. In addition, we also propose a quantitative evaluation method for map updating problem. The result shows our algorithm has a much better performance than the existing approaches.
Process-Driven Data Privacy
Authors: Weiyi Xia, Murat Kantarcioglu, Zhiyu Wan, Raymond Heatherly, Yevgeniy Vorobeychik, Bradley A. Malin
Venue: CIKM
Year: 2015

The quantity of personal data gathered by service providers via our daily activities continues to grow at a rapid pace. The sharing, and the subsequent analysis of, such data can support a wide range of activities, but concerns around privacy often prompt an organization to transform the data to meet certain protection models (e.g., k-anonymity or ε-differential privacy). These models, however, are based on simplistic adversarial frameworks, which can lead to both under- and over-protection. For instance, such models often assume that an adversary attacks a protected record exactly once. We introduce a principled approach to explicitly model the attack process as a series of steps. Specifically, we engineer a factored Markov decision process (FMDP) to optimally plan an attack from the adversary's perspective and assess the privacy risk accordingly. The FMDP captures the uncertainty in the adversary's belief (e.g., the number of identified individuals that match the de-identified data) and enables the analysis of various real world deterrence mechanisms beyond a traditional protection model, such as a penalty for committing an attack. We present an algorithm to solve the FMDP and illustrate its efficiency by simulating an attack on publicly accessible U.S. census records against a real identified resource of over 500,000 individuals in a voter registry. Our results demonstrate that while traditional privacy models commonly expect an adversary to attack exactly once per record, an optimal attack in our model may involve exploiting none, one, or more individuals in the pool of candidates, depending on context.
PlateClick: Bootstrapping Food Preferences Through an Adaptive Visual Interface
Authors: Longqi Yang, Yin Cui, Fan Zhang, John P. Pollak, Serge J. Belongie, Deborah Estrin
Venue: CIKM
Year: 2015

Food preference learning is an important component of wellness applications and restaurant recommender systems as it provides personalized information for effective food targeting and suggestions. However, existing systems require some form of food journaling to create a historical record of an individual's meal selections. In addition, current interfaces for food or restaurant preference elicitation rely extensively on text-based descriptions and rating methods, which can impose high cognitive load, thereby hampering wide adoption.
A Min-Max Optimization Framework For Online Graph Classification
Authors: Peng Yang, Peilin Zhao
Venue: CIKM
Year: 2015

Traditional online learning for graph node classification adapts graph regularization into ridge regression, which may not be suitable when data is adversarially generated. To solve this issue, we propose a more general min-max optimization framework for online graph node classification. The derived online algorithm can achieve a min-max regret compared with the optimal linear model found offline. However, this algorithm assumes that the label is provided for every node, while label is scare and labeling is usually either too time-consuming or expensive in real-world applications. To save labeling effort, we propose a novel confidence-based query approach to prioritize the informative labels. Our theoretical result shows that an online algorithm learning on these selected labels can achieve comparable mistake bound with the fully-supervised online counterpart. To take full advantage of these labels, we propose an aggressive algorithm, which can update the model even if no error occurs. Theoretical analysis shows that the mistake bound of the proposed method, thanks to the aggressive update trials, is better than conservative competitor in expectation. We finally empirically evaluate it on several real-world graph databases. Encouraging experimental results further demonstrate the effectiveness of our method.
Structural Constraints for Multipartite Entity Resolution with Markov Logic Network
Authors: Tengyuan Ye, Hady Wirawan Lauw
Venue: CIKM
Year: 2015

Multipartite entity resolution seeks to match entity mentions across several collections. An entity mention is presumed unique within a collection, and thus could match at most one entity mention in each of the other collections. In addition to domain-specific features considered in entity resolution, there are a number of domain-invariant structural contraints that apply in this scenario, including one-to-one assignment as well as cross-collection transitivity. We propose a principled solution to the multipartite entity resolution problem, building on the foundation of Markov Logic Network (MLN) that combines probabilistic graphical model and first-order logic. We describe how the domain-invariant structural constraints could be expressed appropriately in terms of Markov logic, flexibly allowing joint modeling with domain-specific features. Experiments on two real-life datasets, each spanning four collections, show the utility of this approach and validate the contributions of various MLN components.
Rank Consistency based Multi-View Learning: A Privacy-Preserving Approach
Authors: Han-Jia Ye, De-Chuan Zhan, Yuan Miao, Yuan Jiang, Zhi-Hua Zhou
Venue: CIKM
Year: 2015

Complex media objects are often described by multi-view feature groups collected from diverse domains or information channels. Multi-view learning, which attempts to exploit the relationship among multiple views to improve learning performance, has drawn extensive attention. It is noteworthy that in some real-world applications, features of different views may come from different private data repositories, and thus, it is desired to exploit view relationship with data privacy preserved simultaneously. Existing multi-view learning approaches such as subspace methods and pre-fusion methods are not applicable in this scenario because they need to access the whole features, whereas late-fusion approaches could not exploit information from other views to improve the individual view-specific learners. In this paper, we propose a novel multi-view learning framework which works in a hybrid fusion manner. Specifically, we convert predicted values of each view into an Accumulated Prediction Matrix (APM) with low-rank constraint enforced jointly by the multiple views. The joint low-rank constraint enables the view-specific learner to exploit other views to help improve the performance, without accessing the features of other views. Thus, the proposed RANC framework provides a privacy-preserving way for multi-view learning. Furthermore, we consider variants of solutions to achieve rank consistency and present corresponding methods for the optimization. Empirical investigations on real datasets show that the proposed method achieves state-of-the-art performance on various tasks.
Incomplete Multi-view Clustering via Subspace Learning
Authors: Qiyue Yin, Shu Wu, Liang Wang
Venue: CIKM
Year: 2015

Multi-view clustering, which explores complementary information between multiple distinct feature sets for better clustering, has a wide range of applications, e.g., knowledge management and information retrieval. Traditional multi-view clustering methods usually assume that all examples have complete feature sets. However, in real applications, it is often the case that some examples lose some feature sets, which results in incomplete multi-view data and notable performance degeneration. In this paper, a novel incomplete multi-view clustering method is therefore developed, which learns unified latent representations and projection matrices for the incomplete multi-view data. To approximate the high level scaled indicator matrix defined to represent class label matrix, the latent representations are expected to be non-negative and column orthogonal. Besides, since data are often with high dimensional and noisy features, the projection matrices are enforced to be sparse so as to select relevant features when learning the latent space. Furthermore, the inter-view and intra-view data structure is preserved to further enhance the clustering performance. To these ends, an objective is developed with efficient optimization strategy and convergence analysis. Extensive experiments demonstrate that our model performs better than the state-of-the-art multi-view clustering methods in various settings.
A Cost-based Method for Location-Aware Publish/Subscribe Services
Authors: Minghe Yu, Guoliang Li, Jianhua Feng
Venue: CIKM
Year: 2015

Location-based services have attracted significant attentions from both industry and academia, thanks to modern smartphones and mobile Internet. To provide users with gratifications, location-aware publish/subscribe has been recently proposed, which delivers spatio-textual messages of publishers to subscribers whose registered spatio-textual subscriptions are relevant to the messages. Since there could be large numbers of subscriptions, it is necessary to devise an efficient location-aware publish/subscribe system to enable instant message filtering. To this end, in this paper we propose two novel indexing structures, mbrtrie and PKQ. Using the indexes, we devise two filtering algorithms to support fast message filtering. We analyze the complexities of the two filtering algorithms and develop a cost-based model to judiciously select the best filtering algorithm for different scenarios. The experimental results show that our method achieves high performance and significantly outperforms the baseline approaches
Gauging Correct Relative Rankings For Similarity Search
Authors: Weiren Yu, Julie A. McCann
Venue: CIKM
Year: 2015

One of the important tasks in link analysis is to quantify the similarity between two objects based on hyperlink structure. SimRank is an attractive similarity measure of this type. Existing work mainly focuses on absolute SimRank scores, and often harnesses an iterative paradigm to compute them. While these iterative scores converge to exact ones with the increasing number of iterations, it is still notoriously difficult to determine how well the relative orders of these iterative scores can be preserved for a given iteration. In this paper, we propose efficient ranking criteria that can secure correct relative orders of node-pairs with respect to SimRank scores when they are computed in an iterative fashion. Moreover, we show the superiority of our criteria in harvesting top-K SimRank scores and bucket orders from a full ranking list. Finally, viable empirical studies verify the usefulness of our techniques for SimRank top-K ranking and bucket ordering.
10 Bits of Surprise: Detecting Malicious Users with Minimum Information
Authors: Reza Zafarani, Huan Liu
Venue: CIKM
Year: 2015

Malicious users are a threat to many sites and defending against them demands innovative countermeasures. When malicious users join sites, they provide limited information about themselves. With this limited information, sites can find it difficult to distinguish between a malicious user and a normal user. In this study, we develop a methodology that identifies malicious users with limited information. As information provided by malicious users can vary, the proposed methodology utilizes minimum information to identify malicious users. It is shown that as little as 10 bits of information can help greatly in this challenging task. The experiments results verify that this methodology is effective in identifying malicious users in the realistic scenario of limited information availability.
Learning User Preferences for Topically Similar Documents
Authors: Mustafa Zengin, Ben Carterette
Venue: CIKM
Year: 2015

Similarity measures have been used widely in information retrieval research. Most research has been done on query-document or document-document similarity without much attention to the user's perception of similarity in the context of the information need. In this study, we collect user preference judgements of web document similarity in order to investigate: (1) the correlation between similarity measures and users' perception of similarity, (2) the correlation between the web document features plus document-query features and users' similarity judgements. We analyze the performance of various similarity methods at predicting user preferences, in both unsupervised and supervised settings. We show that a supervised approach using many features is able to predict user preferences close to the level of agreement between users, and moreover achieve a 15% improvement in AUC over an unsupervised approach.
Range Search on Uncertain Trajectories
Authors: Liming Zhan, Ying Zhang, Wenjie Zhang, Xiaoyang Wang, Xuemin Lin
Venue: CIKM
Year: 2015

The range search on trajectories is fundamental in a wide spectrum of applications such as environment monitoring and location based services. In practice, a large portion of spatio-temporal data in the above applications is generated with low sampling rate and the uncertainty arises between two subsequent observations of a moving object. To make sense of the uncertain trajectory data, it is critical to properly model the uncertainty of the trajectories and develop efficient range search algorithms on the new model. Assuming uncertain trajectories are modeled by the popular Markov Chains, in this paper we investigate the problem of range search on uncertain trajectories. In particular, we propose a general framework for range search on uncertain trajectories following the filtering-and-refinement paradigm where summaries of uncertain trajectories are constructed to facilitate the filtering process. Moreover, statistics based and partition based filtering techniques are developed to enhance the filtering capabilities. Comprehensive experiments demonstrate the effectiveness and efficiency of our new techniques.
ORec: An Opinion-Based Point-of-Interest Recommendation Framework
Authors: Jia-Dong Zhang, Chi-Yin Chow, Yu Zheng
Venue: CIKM
Year: 2015

As location-based social networks (LBSNs) rapidly grow, it is a timely topic to study how to recommend users with interesting locations, known as points-of-interest (POIs). Most existing POI recommendation techniques only employ the check-in data of users in LBSNs to learn their preferences on POIs by assuming a user's check-in frequency to a POI explicitly reflects the level of her preference on the POI. However, in reality users usually visit POIs only once, so the users' check-ins may not be sufficient to derive their preferences using their check-in frequencies only. Actually, the preferences of users are exactly implied in their opinions in text-based tips commenting on POIs. In this paper, we propose an opinion-based POI recommendation framework called ORec to take full advantage of the user opinions on POIs expressed as tips. In ORec, there are two main challenges: (i) detecting the polarities of tips (positive, neutral or negative), and (ii) integrating them with check-in data including social links between users and geographical information of POIs. To address these two challenges, (1) we develop a supervised aspect-dependent approach to detect the polarity of a tip, and (2) we devise a method to fuse tip polarities with social links and geographical information into a unified POI recommendation framework. Finally, we conduct a comprehensive performance evaluation for ORec using two large-scale real data sets collected from Foursquare and Yelp. Experimental results show that ORec achieves significantly superior polarity detection and POI recommendation accuracy compared to other state-of-the-art polarity detection and POI recommendation techniques.
Learning Entity Types from Query Logs via Graph-Based Modeling
Authors: Jingyuan Zhang, Luo Jie, Altaf Rahman, Sihong Xie, Yi Chang, Philip S. Yu
Venue: CIKM
Year: 2015

Entities (e.g., person, movie or place) play an important role in real-world applications and learning entity types has attracted much attention in recent years. Most conventional automatic techniques use large corpora, such as news articles, to learn types of entities. However, such text corpora focus on general knowledge about entities in an objective way. Hence, it is difficult to satisfy those users with specific and personalized needs for an entity. Recent years have witnessed an explosive expansion in the mining of search query logs, which contain billions of entities. The word patterns and click-throughs in search logs are not found in text corpora, thus providing a complemental source for discovering entity types based on user behaviors. In this paper, we study the problem of learning entity types from search query logs and address the following challenges: (1) queries are short texts, and information related to entities is usually very sparse; (2) large amounts of irrelevant information exists in search logs, bringing noise in detecting entity types. In this paper, we first model query logs using a bipartite graph with entities and their auxiliary information, such as contextual words and clicked URLs. Then we propose a graph-based framework called ELP (Ensemble framework based on Lable Propagation) to simultaneously learn the types of both entities and auxiliary signals. In ELP, two separate strategies are designed to fix the problems of sparsity and noise in query logs. Extensive empirical studies are conducted on real search logs to evaluate the effectiveness of the proposed ELP framework.
Enterprise Social Link Recommendation
Authors: Jiawei Zhang, Yuanhua Lv, Philip S. Yu
Venue: CIKM
Year: 2015

Many companies have started to use Enterprise Social Networks (ESNs), such as Yammer, to facilitate collaboration and communication amongst their employees in the business context. Social link recommendation, which finds and suggests whom one wants to connect with in a company, is crucial for ESNs to promote their usages. Although link recommendation has been studied extensively in external social networks (e.g., Facebook and Twitter), it has not been addressed in ESNs. In this paper, we study this novel problem. Social link recommendation in ESNs is significantly different from that in external social networks, and also has unique challenges: (1) people usually socialize differently in enterprise than in their personal life, but users' social behaviors in enterprise have not been well explored, and (2) there is important business information available in ESNs under the enterprise context, e.g., a company?s organizational chart, but how to exploit it for link recommendation is still an open problem. To this end, we mine not only the social graph and user-generated content in ESNs, but also the company's organizational chart, to model enterprise user social behaviors. We develop a supervised link recommendation algorithm using a large scale enterprise social network based on Yammer (with over 100k users), which shows that the proposed techniques perform effectively. Moreover, we find that both the social graph and the organizational chart are complementary to each other for link recommendation in ESNs.
Improving Label Quality in Crowdsourcing Using Noise Correction
Authors: Jing Zhang, Victor S. Sheng, Jian Wu, Xiaoqin Fu, Xindong Wu
Venue: CIKM
Year: 2015

This paper proposes a novel framework that introduces noise correction techniques to further improve label quality after ground truth inference in crowdsourcing. In the framework, an adaptive voting noise correction algorithm (AVNC) is proposed to identify and correct the most likely noises with the help of estimated qualities of labelers provided by the ground truth inference. The experimental results on two real-world datasets show that (1) the framework can improve label quality regardless of inference algorithms, especially under the circumstance that each example has a few noisy labels; and (2) since the algorithm AVNC considers both the number of and the probability of potential noises, it outperforms a baseline noise correction algorithm.
Improving Collaborative Filtering via Hidden Structured Constraint
Authors: Qing Zhang, Houfeng Wang
Venue: CIKM
Year: 2015

Matrix factorization models, as one of the most powerful Collaborative Filtering approaches, have greatly advanced the recommendation tasks. However, few of them are able to explicitly consider structured constraint for modeling user interests. To solve this problem, we propose a novel matrix factorization model with adaptive graph regularization framework, which can automatically discover latent user communities jointly with learning latent user representations, to enhance the discriminative power for recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed method.
Modeling Parameter Interactions in Ranking SVM
Authors: Yaogong Zhang, Jun Xu, Yanyan Lan, Jiafeng Guo, Maoqiang Xie, Yalou Huang, Xueqi Cheng
Venue: CIKM
Year: 2015

Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = ∑(i,j) αij (xi - xj), where αij denotes the Lagrange parameters associated with each pair (i,j). It is obvious that there exist significant interactions over the document pairs because two preference pairs could share a same document as their items. Thus it is natural to ask if there also exist interactions over the model parameters αij, which we may leverage to propose better ranking model. This paper aims to answer the question. Firstly, we found that there exists a low-rank structure over the Ranking SVM model parameters αij, which indicates that the interactions do exist. Then, based on the discovery, we made a modification on the original Ranking SVM model by explicitly applying a low-rank constraint to the parameters. Specifically, each parameter αij is decomposed as a product of two low-dimensional vectors, i.e., αij = vi, vj, where vectors vi and vj correspond to document i and j, respectively. The learning process, thus, becomes to optimize the modified dual form objective function with respect to the low-dimensional vectors. Experimental results on three LETOR datasets show that our method, referred to as Factorized Ranking SVM, can outperform state-of-the-art baselines including the conventional Ranking SVM.
Sentiment Extraction by Leveraging Aspect-Opinion Association Structure
Authors: Li Zhao, Minlie Huang, Jiashen Sun, Hengliang Luo, Xiankai Yang, Xiaoyan Zhu
Venue: CIKM
Year: 2015

Sentiment extraction aims to extract and group the task of extracting and grouping aspect and opinion words from online reviews. Previous works usually extract aspect and opinion words by leveraging association between a single pair of aspect and opinion word[5] [14] [9] [4][11], but the structure of aspect and opinion word clusters has not been fully exploited.