OpenRaiser/Intern-Atlas huggingface dataset:
CoDEM: An Ingenious Tool of Insight into Community Detection in Social Networks
Authors: Meng Wang, Chaokun Wang, Jun Chen
Venue: CIKM
Year: 2014

In recent years, community structure has attracted increasing attention in social network analysis. However, performances of multifarious approaches to community detection are seldom evaluated in a suite of systematic measurements. Furthermore, we can hardly find works which reveal diverse features based on the detected community structure. In this paper, we build a tool called CoDEM to make both quality evaluations of community detection and an in-depth mining for pivotal nodes inside communities. This tool integrates several effective approaches to community detection, establishes an overall evaluation system and gets the multi-dimensional ranking for the local importance of nodes. Moreover, the tool is built with a friendly user interface.
Concept-based Short Text Classification and Ranking
Authors: Fang Wang, Zhongyuan Wang, Zhoujun Li, Ji-Rong Wen
Venue: CIKM
Year: 2014

Most existing approaches for text classification represent texts as vectors of words, namely ``Bag-of-Words.'' This text representation results in a very high dimensionality of feature space and frequently suffers from surface mismatching. Short texts make these issues even more serious, due to their shortness and sparsity. In this paper, we propose using ``Bag-of-Concepts'' in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem. Based on ``Bag-of-Concepts,'' a novel framework is proposed for lightweight short text classification applications. By leveraging a large taxonomy knowledgebase, it learns a concept model for each category, and conceptualizes a short text to a set of relevant concepts. A concept-based similarity mechanism is presented to classify the given short text to the most similar category. One advantage of this mechanism is that it facilitates short text ranking after classification, which is needed in many applications, such as query or ad recommendation. We demonstrate the usage of our proposed framework through a real online application: Channel-based Query Recommendation. Experiments show that our framework can map queries to channels with a high degree of precision (avg. precision=90.3%), which is critical for recommendation applications.
TwinChat: A Twitter and Web User Interactive Chat System
Authors: Yuanyuan Wang, Gouki Yasui, Yuji Hosokawa, Yukiko Kawai, Toyokazu Akiyama, Kazutoshi Sumiya
Venue: CIKM
Year: 2014

This paper presents TWinChat, a Twitter and Web user interactive chat system to support simultaneous communication between microbloggers and Web users in real-time through both the contents of microblogs and Web pages. TWinChat provides a question answering interface attached to Web pages, which allows Web users to chat with Twitter users in real-time while presenting tweets that are associated with Web pages, i.e., simultaneous cross-media communication. In order to map heterogeneous media, the system extracts relationship between tweets and Web pages by generating queries based on location names. Thus, our system can effectively present messages from Web users to help Twitter users immediately obtain useful information or knowledge, and it also can effectively present tweets from the Twitter users to help the Web users easily grasp the current situation in real-time.
SharkDB: An In-Memory Column-Oriented Trajectory Storage
Authors: Haozhou Wang, Kai Zheng, Jiajie Xu, Bolong Zheng, Xiaofang Zhou, Shazia Sadiq
Venue: CIKM
Year: 2014

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a high volume of trajectory data representing the motion history of moving objects. However some characteristics of trajectories such as variable lengths and asynchronous sampling rates make it difficult to fit into traditional database systems that are disk-based and tuple-oriented. Motivated by the success of column store and recent development of in-memory databases, we try to explore the potential opportunities of boosting the performance of trajectory data processing by designing a novel trajectory storage within main memory. In contrast to most existing trajectory indexing methods that keep consecutive samples of the same trajectory in the same disk page, we partition the database into frames in which the positions of all moving objects at the same time instant are stored together and aligned in main memory. We found this column-wise storage to be surprisingly well suited for in-memory computing since most frames can be stored in highly compressed form, which is pivotal for increasing the memory throughput and reducing CPU-cache miss. The independence between frames also makes them natural working units when parallelizing data processing on a multi-core environment. Lastly we run a variety of common trajectory queries on both real and synthetic datasets in order to demonstrate advantages and study the limitations of our proposed storage.
Enabling Precision/Recall Preferences for Semi-supervised SVM Training
Authors: Zeyi Wen, Rui Zhang, Kotagiri Ramamohanarao
Venue: CIKM
Year: 2014

Semi-supervised learning is an essential approach to classification when the available labeled data is insufficient and we need to also make use of unlabeled data in the learning process. Numerous research efforts have focused on designing algorithms to improve the F1 score, but have any mechanism to control precision or recall individually. However, many applications have precision/recall preferences. For instance, an email spam classifier requires a precision of 0.9 to mitigate the false dismissal of useful emails. In this paper, we propose a method that allows to specify a precision/recall preference while maximising the F1 score. Our key idea is that we divide the semi-supervised learning process into multiple rounds of supervised learning, and the classifier learned at each round is calibrated using a sub-set of the labeled dataset before we use it on the unlabeled dataset for enlarging the training dataset. Our idea is applicable to a number of learning models such as Support Vector Machines (SVMs), Bayesian networks and neural networks. We focus our research and the implementation of our idea on SVMs. We conduct extensive experiments to validate the effectiveness of our method. The experimental results show that our method can train classifiers with a precision/recall preference, while the popular semi-supervised SVM training algorithm (which we use as the baseline) cannot. When we specify the precision preference and the recall preference to be the same, which indicates to maximise the F1 score only as the baseline does, our method achieves better or similar F1 scores to the baseline. An additional advantage of our method is that it converges much faster than the baseline.
Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses
Authors: Miaomiao Wen, Carolyn Penstein Rosé
Venue: CIKM
Year: 2014

MOOCs attract diverse users with varying habits. Identifying those patterns through clickstream analysis could enable more effective personalized support for student information seeking and learning in that online context. We propose a novel method to characterize types of sessions in MOOCs by mining the habitual behaviors of students within individual sessions. We model learning sessions as a distribution of activities and activity sequences with a topical N-gram model. The representation offers insights into what groupings of habitual student behaviors are associated with higher or lower success in the course. We also investigate how context information, such as time of day or a user's demographic information, is associated with the types of learning sessions.
Ranking Optimization with Constraints
Authors: Fangzhao Wu, Jun Xu, Hang Li, Xin Jiang
Venue: CIKM
Year: 2014

This paper addresses the problem of post-processing of ranking in search, referred to as post ranking. Although important, no research seems to have been conducted on the problem, particularly with a principled approach, and in practice ad-hoc ways of performing the task are being adopted. This paper formalizes the problem as constrained optimization in which the constraints represent the post-processing rules and the objective function represents the trade-off between adherence to the original ranking and satisfaction of the rules. The optimization amounts to refining the original ranking result based on the rules. We further propose a specific probabilistic implementation of the general formalization on the basis of the Bradley-Terry model, which is theoretically sound, effective, and efficient. Our experimental results, using benchmark datasets and enterprise search dataset, show that the proposed method works much better than several baseline methods of utilizing rules.
Document Prioritization for Scalable Query Processing
Authors: Hao Wu, Hui Fang
Venue: CIKM
Year: 2014

Query latency is an important performance measure of any search engines because it directly affects search users' satisfaction. The key challenge is how to efficiently retrieve top-K ranked results for a query. Current search engines process queries in either the conjunctive or disjunctive modes. However, there is still a large performance gap between these two modes since the conjunctive mode is more efficient with lower search accuracy while the disjunctive mode is more effective but requires more time to process the queries.
Analytical Performance Modeling for Top-K Query Processing
Authors: Hao Wu, Hui Fang
Venue: CIKM
Year: 2014

Top-K query processing is one of the most important problems in large-scale Information Retrieval systems. Since query processing time varies for different queries, an accurate run-time performance prediction is critical for online query scheduling and load balancing, which could eventually reduce the query waiting time and improve the throughput. Previous studies estimated the query processing time based on the combination of term-level features. Unfortunately, these features were often selected arbitrarily, and the linear combination of these features might not be able to accurately capture the complexity in the query processing.
Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning
Authors: Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang
Venue: CIKM
Year: 2014

In traditional multi-instance learning (MIL), instances are typically represented by using a single feature view. As MIL becoming popular in domain specific learning tasks, aggregating multiple feature views to represent multi-instance bags has recently shown promising results, mainly because multiple views provide extra information for MIL tasks. Nevertheless, multiple views also increase the risk of involving redundant views and irrelevant features for learning. In this paper, we formulate a new cross-view feature selection problem that aims to identify the most representative features across all feature views for MIL. To achieve the goal, we design a new optimization problem by integrating both multi-view representation and multi-instance bag constraints. The solution to the objective function will ensure that the identified top-m features are the most informative ones across all feature views. Experiments on two real-world applications demonstrate the performance of the cross-view feature selection for content-based image retrieval and social media content recommendation.
Towards Consistency Checking over Evolving Ontologies
Authors: Jiewen Wu, Freddy Lécué
Venue: CIKM
Year: 2014

Data captured in OWL ontologies is generally considered to be more prone to changes than the schema in many situations. Such changes often necessitate consistency checking over the resulting ontologies in order to maintain coherent knowledge, specifically in dynamic settings. In this paper, we present an approach to check the consistency over an evolving ontology resulting from data insertions and deletions, given by some expressive underlying Description Logic dialect. The approach, assuming an initially consistent ontology, works by syntactically identifying "relevant" and representative parts of the data for the given updates, i.e., the part that may contribute to subsequent consistency checking. Our approach has demonstrated its efficacy in checking consistency over large and real-world ontologies and outperforms existing approaches in several circumstances.
Searching Locally-Defined Entities
Authors: Zhaohui Wu, Yuanhua Lv, Ariel Fuxman
Venue: CIKM
Year: 2014

When consuming content, users typically encounter entities that they are not familiar with. A common scenario is when users want to find information about entities directly within the content they are consuming. For example, when reading the book "Adventures of Huckleberry Finn", a user may lose track of the character Mary Jane and want to find some paragraph in the book that gives relevant information about her. The way this is achieved today is by invoking the ubiquitous Find function ("Ctrl-F"). However, this only returns exact-matching results without any relevance ranking, leading to a suboptimal user experience.
SemStore: A Semantic-Preserving Distributed RDF Triple Store
Authors: Buwen Wu, Yongluan Zhou, Pingpeng Yuan, Hai Jin, Ling Liu
Venue: CIKM
Year: 2014

The flexibility of the RDF data model has attracted an increasing number of organizations to store their data in an RDF format. With the rapid growth of RDF datasets, we envision that it is inevitable to deploy a cluster of computing nodes to process large-scale RDF data in order to deliver desirable query performance. In this paper, we address the challenging problems of data partitioning and query optimization in a scale-out RDF engine. We identify that existing approaches only focus on using fine-grained structural information for data partitioning, and hence fail to localize many types of complex queries. We then propose a radically different approach, where a coarse-grained structure, namely Rooted Sub-Graph (RSG), is used as the partition unit. By doing so, we can capture structural information at a much greater scale and hence are able to localize many complex queries. We also propose a k-means partitioning algorithm for allocating the RSGs onto the computing nodes as well as a query optimization strategy to minimize the inter-node communication during query processing. An extensive experimental study using benchmark datasets and real dataset shows that our engine, SemStore, outperforms existing systems by orders of magnitudes in terms of query response time.
A Cross-modal Multi-task Learning Framework for Image Annotation
Authors: Liang Xie, Peng Pan, Yansheng Lu, Shixun Wang
Venue: CIKM
Year: 2014

With the advance of internet, multi-modal data can be easily collected from many social websites such as Wikipedia, Flickr, YouTube, etc. Images shared on the web are usually associated with social tags or other textual information. Although existing multi-modal methods can make use of associated text to improve image annotation, the disadvantages of them are that associated text is also required for a new image to be predicted. In this paper, we propose the cross-modal multi-task learning (CMMTL) framework for image annotation. Labeled and unlabeled multi-modal data are both levaraged for training in CMMTL, and it finally obtains visual classifiers which can predict concepts for a single image without any associated information. CMMTL integrates graph learning, multi-task learning and cross-modal learning into a joint framework, where a shared subspace is learned to preserve both cross-modal correlation and concept correlation. The optimal solution of the proposed framework can be obtained by solving a generalized eigenvalue problem. We conduct comprehensive experiments on two real world image datasets: MIR Flickr and NUS-WIDE, to evaluate the performance of the proposed framework. Experimental results demonstrate that CMMTL obtains a significant improvement over several representative methods for cross-modal image annotation.
A Demonstration of SearchonTS: An Efficient Pattern Search Framework for Time Series Data
Authors: Xiaomin Xu, Sheng Huang, Yaoliang Chen, Chen Wang, Inge Halilovic, Kevin Brown, Mark Ashworth
Venue: CIKM
Year: 2014

In recent years, time series data are everywhere across different industry, which creates a huge demand on time series data analysis, such as pattern search. Meanwhile, it is increasingly realized that only when pattern search results together with information from relational tables could be used in a programming-free way, can they perform analysis on time series conveniently. Hence, casual users highly demand that queries involving pattern search could be performed via SQLs. However, existing database products supporting time series data type lack the capability to perform pattern searches on time series data. This paper presents SearchonTS, an extendable framework for in-database pattern search on time series data. It provides a series of interfaces so that time series index and pattern search can be added and performed in a uniformed and query optimized manner. SearchonTS is implemented as an extension on Informix, which is a database product in IBM software product series. It targets a future release of IBM Informix. We have implemented index-based pattern search for Euclid Distance(ED) via SearhonTS to demonstrate its usability for developers. And real scenario is also provided to show SQL involving pattern search so that users can have a more clear experience of the convenience.
Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups
Authors: Yinqing Xu, Wai Lam, Tianyi Lin
Venue: CIKM
Year: 2014

Most collaborative filtering (CF) algorithms only make use of the rating scores given by users for items. However, it is often the case that each rating score is associated with a piece of review text. Such review texts, which are capable of providing us valuable information to reveal the reasons why users give a certain rating, have not been exploited and they are usually ignored by most CF algorithms. Moreover, the underlying relationship buried in users and items has not been fully exploited. Items we would recommend can often be characterized into hidden groups (e.g. comedy, horror movie and action movie), and users can also be organized as hidden communities. We propose a new generative model to predict user's ratings on previously unrated items by considering review texts as well as hidden user communities and item groups relationship. Regarding the rating scores, traditional algorithms would not perform well on uncovering the community and group information of each user and each item since the user-item rating matrix is dyadic involving the mutual interactions between users and items. Instead, co-clustering, which is capable of conducting simultaneous clustering of two variables, is able to take advantage of such user-item relationships to better predict the rating scores. Additionally, co-clustering would be more effective for modeling the generation of review texts since different user communities would discuss different topics and vary their own wordings or expression patterns when dealing with different item groups. Besides, by modeling as a mixed membership over community and group respectively, each user or item can belong to multiple communities or groups with varying degrees. We have conducted extensive experiments to predict the missing rating scores on 22 real word datasets. The experimental results demonstrate the superior performance of our proposed model comparing with the state-of-the-art methods.
Online User Location Inference Exploiting Spatiotemporal Correlations in Social Streams
Authors: Yuto Yamaguchi, Toshiyuki Amagasa, Hiroyuki Kitagawa, Yohei Ikawa
Venue: CIKM
Year: 2014

The location profiles of social media users are valuable for various applications, such as marketing and real-world analysis. As most users do not disclose their home locations, the problem of inferring home locations has been well studied in recent years. In fact, most existing methods perform batch inference using static (i.e., pre-stored) social media contents. However, social media contents are generated and delivered in real-time as social streams. In this situation, it is important to continuously update current inference results based on the newly arriving contents to improve the results over time. Moreover, it is effective for location inference to use the spatiotemporal correlation between contents and locations. The main idea of this paper is that we can infer the locations of users who simultaneously post about a local event (e.g., earthquakes). Hence, in this paper, we propose an online location inference method over social streams that exploits the spatiotemporal correlation, achieving 1) continuous updates with low computational and storage costs, and 2) better inference accuracy than that of existing methods. The experimental results using a Twitter dataset show that our method reduces the inference error to less than 68% of existing methods. The results also show that the proposed method can update inference results in constant time regardless of the amount of accumulated contents.
A Fragment-Based Similarity Measure for Concept Hierarchies and Ontologies
Authors: Hui Yang
Venue: CIKM
Year: 2014

Despite the popularity of concept hierarchies and ontologies, such as Yahoo! Directory, a similarity measure that considers both hierarchy content and topology and is highly efficient has not yet been reached. A commonly used metric, Tree Edit Distance, exhibits extreme inefficiency when measuring similarities between unordered hierarchies. In this paper, we propose a novel and feasible solution, Fragment-based Similarity (FBS), to serve as an efficient and effective measurement for hierarchy similarity evaluation. Experimental results and a user study show that FBS not only well-approximates but also is more efficient than Tree Edit Distance
Spatial Verification for Scalable Mobile Image Retrieval
Authors: Xiyu Yang, Xueming Qian
Venue: CIKM
Year: 2014

Owing to the portable and excellent phone camera, people now prefer to take photos and upload them by mobile phone. Content based image retrieval is effective for users to obtain relevant information about a photo. Taking the limited bandwidth and instability into account, we propose an effective scalable mobile image retrieval approach in this paper. The proposed mobile image retrieval algorithm first determines the relevant photos according to visual similarity in mobile end, then mines salient visual words by exploring saliency from multiple relevant images, and finally we determine the contribution order of salient visual words for scalable retrieval. In server terminal, spatial verification is performed to re-rank the results. Compared to the existing approaches of mobile image retrieval, our approach transmits less data and reduces the computational cost of spatial verification. Most importantly, when the bandwidth is limited, we can transmit a part of features according their contributions to retrieval. Experimental results show the effectiveness of the proposed approach.