OpenRaiser/Intern-Atlas huggingface dataset:
Constrained Question Recommendation in MOOCs via Submodularity
Authors: Diyi Yang, Jingbo Shang, Carolyn Penstein Rosé
Venue: CIKM
Year: 2014

A recent area in which recommender systems have shown their value is in online discussion forums and question-answer sites. Earlier work in this space has focused on the problem of matching participants to opportunities but has not adequately addressed the problem that in these social contexts, multiple dimensions of constraints must be satisfied, including limitations on capacity and minimal requirements for expertise. In this work, we propose such a constrained question recommendation problem with load balance constraints in discussion forums and use flow based model to generate the optimal solution. In particular, to address the introduced computation complexity, we investigate the concept of submodularity of the objective function and propose a specific submodular method to give an approximated solution. We present experiments conducted on two Massive Open Online Course (MOOC) discussion forum datasets, and demonstrate the effectiveness and efficiency of our submodular method in solving constrained question recommendation tasks.
Active Learning for Streaming Networked Data
Authors: Zhilin Yang, Jie Tang, Yutao Zhang
Venue: CIKM
Year: 2014

Mining high-speed data streams has become an important topic due to the rapid growth of online data. In this paper, we study the problem of active learning for streaming networked data. The goal is to train an accurate model for classifying networked data that arrives in a streaming manner by querying as few labels as possible. The problem is extremely challenging, as both the data distribution and the network structure may change over time. The query decision has to be made for each data instance sequentially, by considering the dynamic network structure. We propose a novel streaming active query strategy based on structural variability. We prove that by querying labels we can monotonically decrease the structural variability and better adapt to concept drift. To speed up the learning process, we present a network sampling algorithm to sample instances from the data stream, which provides a way for us to handle large volume of streaming data. We evaluate the proposed approach on four datasets of different genres: Weibo, Slashdot, IMDB, and ArnetMiner. Experimental results show that our model performs much better (+5-10% by F1-score on average) than several alternative methods for active learning over streaming networked data.
Exploring Tag-Free RFID-Based Passive Localization and Tracking via Learning-Based Probabilistic Approaches
Authors: Lina Yao, Wenjie Ruan, Quan Z. Sheng, Xue Li, Nickolas J. G. Falkner
Venue: CIKM
Year: 2014

RFID-based localization and tracking has some promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed. In this paper, we investigate a tag-free indoor localizing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment. We formulate localization as a classification task. In particular, we model the received signal strength indicator (RSSI) of passive tags using multivariate Gaussian Mixture Model (GMM), and use the Expectation Maximization (EM) to learn the maximum likelihood estimates of the model parameters. Several other learning-based probabilistic approaches are also explored in the localization problem. To track a moving subject, we propose GMM based Hidden Markov Model (HMM) and k Nearest Neighbor (kNN) based HMM approaches. We conduct extensive experiments in a testbed formed by passive RFID tags, and the experimental results demonstrate the effectiveness and accuracy of our approach.
Keeping You in the Loop: Enabling Web-based Things Management in the Internet of Things
Authors: Lina Yao, Quan Z. Sheng, Anne H. H. Ngu, Byron J. Gao
Venue: CIKM
Year: 2014

Internet of Things (IoT) is an emerging paradigm where physical objects are connected and communicated over the Web. Its capability in assimilating the virtual world and the physical one offers many exciting opportunities. However, how to realize a smooth, seamless integration of the two worlds remains an interesting and challenging topic. In this paper, we showcase an IoT prototype system that enables seamless integration of the virtual and the physical worlds and efficient management of things of interest (TOIs), where services and resources offered by things can be easily monitored, visualized, and aggregated for value-added services by users. This paper presents the motivation, system design, implementation, and demonstration scenario of the system.
A Generative Model for Generating Relevance Labels from Human Judgments and Click-Logs
Authors: Xugang Ye, Jingjing Li, Zijie Qi, Bingyue Peng, Dan Massey
Venue: CIKM
Year: 2014

Lack of high quality relevance labels is a common challenge in the early stage of search engine development. In media search, due to the high recruiting and training cost, the labeling process is usually conducted by a small number of human judges. Consequently, the generated labels are often limited and biased. On the contrary, the click data that is extracted from a large population of real users is massive and less biased. However, the click data also contains considerable noise. Therefore, more and more researchers have begun to focus on combining those two resources to generate a better ground-truth approximation. In this paper, we present a novel method of generating the relevance labels for media search. The method is based on a generative model that considers human judgment, position, and click status as observations generated from a hidden relevance with multinomial prior. The model considers the position bias with a requirement that the click status depends on both the hidden relevance and the position. We infer the model parameters by using a Gibbs sampling procedure with hyper-parameter optimization. From experiments on the Xbox's data, the newly inferred relevance labels significantly increase the data volume for ranker training and have demonstrated superior performance compared to using the limited human labels only, the click-through-rates only, and the heuristic combination of the two.
Negative FaceBlurring: A Privacy-by-Design Approach to Visual Lifelogging with Google Glass
Authors: Tengqi Ye, Brian Moynagh, Rami Albatal, Cathal Gurrin
Venue: CIKM
Year: 2014

Wearable devices such as Google Glass are receiving increasing attention and look set to become part of our technical landscape over the next few years. At the same time, lifelogging is a topic that is growing in popularity with a host of new devices on the market that visually capture life experience in an automated manner. In this paper, we describe a visual lifelogging solution for Google Glass that is designed to capture life experience in rich visual detail, yet maintain the privacy of unknown bystanders.
Effect of Intent Descriptions on Retrieval Evaluation
Authors: Emine Yilmaz, Evangelos Kanoulas, Nick Craswell
Venue: CIKM
Year: 2014

Test collections play an important role in adhoc and diversity retrieval evaluation. Constructing a test collection for adhoc evaluation involves (1) selecting a set of queries to be judged, (2) selecting an intent (topic) description for that query, and (3) obtaining relevance judgments with respect to the specific intent description for that particular query. Recent work showed that the selection of intents play an important role in the relative performance of retrieval systems for diversity evaluation. However, no previous work has analysed how the choice of a specific intent description may affect adhoc evaluation. We show that intent descriptions have a significant impact in adhoc evaluation and that special care should be given as to how the intent descriptions are selected. We further show that it is better to have very general intent descriptions or no intent descriptions at all when constructing test collections for adhoc evaluation. We then focus on diversity evaluation and identify the effect intent descriptions have on diversity based retrieval evaluation. We quantify this effect and discuss experimental design decisions for the optimal distribution of judgment effort across different intents for a query vs. different queries.
Search Result Diversification via Filling Up Multiple Knapsacks
Authors: Hai-Tao Yu, Fuji Ren
Venue: CIKM
Year: 2014

Result diversification is a topic of great value for enhancing user experience in many fields, such as web search and recommender systems. Many existing methods generate a diversified result in a sequential manner, but they work well only if the preceding choices are optimal or close to the optimal solution. Moreover, a manually tuned parameter (say,λ) is often required to trade off relevance and diversity. This makes it difficult to know whether the failures are caused by the optimization criterion or the setting of λ. In context of web search, we formulate the result diversification task as a 0-1 multiple subtopic knapsack problem (MSKP), where a subset of documents are optimally chosen like filling up multiple subtopic knapsacks. This formulation yields no trade-off parameters to be specified beforehand. Solving the 0-1 MSKP is NP-hard, we treat the optimization of 0-1 MSKP using a graphical model over latent binary variables as a maximum posterior inference problem, and tackle it with the max-sum belief propagation algorithm. To validate the effectiveness and efficiency of the proposed 0-1 MSKP model, we conduct a series of experiments on two TREC diversity collections. The experimental results show that the proposed model outperforms several state-of-the-art methods significantly, not only in terms of standard diversity metrics (α-nDCG, nERRIA and subtopic recall), but also in terms of efficiency.
Distributed Stochastic ADMM for Matrix Factorization
Authors: Zhi-Qin Yu, Xing-Jian Shi, Ling Yan, Wu-Jun Li
Venue: CIKM
Year: 2014

Matrix factorization (MF) has become the most popular technique for recommender systems due to its promising performance. Recently, distributed (parallel) MF models have received much attention from researchers of big data community. In this paper, we propose a novel model, called distributed stochastic alternating direction methods of multipliers (DS-ADMM), for large-scale MF problems. DS-ADMM is a distributed stochastic variant of ADMM. In particular, we first devise a new data split strategy to make the distributed MF problem fit for the ADMM framework. Then, a stochastic ADMM scheme is designed for learning. Finally, we implement DS-ADMM based on message passing interface (MPI), which can run on clusters with multiple machines (nodes). Experiments on several data sets from recommendation applications show that our DS-ADMM model can outperform other state-of-the-art distributed MF models in terms of both efficiency and accuracy.
Pattern Match Query in a Large Uncertain Graph
Authors: Ye Yuan, Guoren Wang, Lei Chen
Venue: CIKM
Year: 2014

Many studies have been conducted on seeking an efficient solution for pattern matching over graphs. This interest is largely due to large number of applications in many fields, which require efficient solutions for pattern matching, including protein complex prediction, social network analysis and structural pattern recognition. However, in many real applications, the graph data are often noisy, incomplete, and inaccurate. In other words, there exist many uncertain graphs. Therefore, in this paper, we study pattern matching in a large uncertain graph. Specifically, we want to retrieve all qualified matches of a query pattern in the uncertain graph. Though pattern matching over an uncertain graph is NP-hard, we employ a filtering-and-verification framework to speed up the search. In the filtering phase, we propose a probabilistic matching tree, PM-tree, based on match cuts obtained by a cut selection process. Based on PM-tree, we devise a collective pruning strategy to prune a large number of unqualified matches. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed algorithms.
Rubato DB: A Highly Scalable Staged Grid Database System for OLTP and Big Data Applications
Authors: Li-Yan Yuan, Lengdong Wu, Jia-Huai You, Yan Chi
Venue: CIKM
Year: 2014

This paper proposes a new formula protocol for distributed concurrency control, and specifies a staged grid architecture for highly scalable database management systems. The paper also describes novel implementation techniques of Rubato DB based on the proposed protocol and architecture. We have conducted extensive experiments which clearly show that Rubato DB is highly scalable with efficient performance under both TPC-C and YCSB benchmarks. Our paper verifies that the formula protocol and the staged grid architecture provide a satisfactory solution to one of the important challenges in the database systems: to develop a highly scalable database management system that supports various consistency levels from ACID to BASE.
ExpressQ: Identifying Keyword Context and Search Target in Relational Keyword Queries
Authors: Zhong Zeng, Zhifeng Bao, Thuy Ngoc Le, Mong-Li Lee, Tok Wang Ling
Venue: CIKM
Year: 2014

Keyword search in relational databases has gained popularitydue to its ease of use. However, the challenge to returnquery answers that satisfy users information need remains.Traditional keyword queries have limited expressive capabilityand are ambiguous. In this work, we extend keywordqueries to enhance their expressive power and describe ansemantic approach to process these queries. Our approachconsiders keywords that match meta-data such as the namesof relations and attributes, and utilizes them to provide thecontext of subsequent keywords in the query. Based on theORM schema graph which captures the semantics of objectsand relationships in the database, we determine the objectsand relationships referred to by the keywords in order to inferthe search target of the query. Then, we construct a setof minimal connected graphs called query patterns, to representusers possible search intentions. Finally, we translatethe top-k ranked query patterns into SQL statements in orderto retrieve information that the user is interested in. Wedevelop a system prototype called ExpressQ to process theextended keyword queries. Experimental results show thatour system is able to generate SQL statements that retrieveuser intended information effectively.
Web-KR 2014: The 5th International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning
Authors: Yi Zeng, Spyros Kotoulas, Zhisheng Huang
Venue: CIKM
Year: 2014

We organize and present the 5th version of the International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning (Web-KR 2014) as a continuous effort to discuss and provide possible theories and techniques to deal with the barriers for knowledge processing at Web scale. This workshop was held in conjunction with the 2014 ACM International Conference on Information and Knowledge Management (CIKM 2014) on November 3rd, 2014 in Shanghai, China. Compared to previous workshops under the same title, accepted papers of this workshop covers even wider topics in the field. The contributions focus on semantic knowledge extraction, representation, knowledge clustering, inconsistency checking, entity relatedness and linking, query suggestions, etc. Many new approaches are proposed to investigate these topics in the context of Web-scale resources. This summary introduces the major contributions of accepted papers in the Web-KR 2014 workshop.
MaC: A Probabilistic Framework for Query Answering with Machine-Crowd Collaboration
Authors: Chen Jason Zhang, Lei Chen, Yongxin Tong
Venue: CIKM
Year: 2014

The popularity of crowdsourcing has recently brought about brand new opportunities for engaging human intelligence in the process of data analysis. Most existing works on crowdsourcing have developed sophisticated methods to utilize the crowd as a new kind of processor, a.k.a. Human Processor Units (HPU). In this paper, we propose a framework, called MaC, to combine the powers of both CPUs and HPUs. In order to build MaC, we need to tackle the following two challenges: (1) HIT Selection: Selecting the "right" HITs (Human Intelligent Tasks) can help reducing the uncertainty significantly and the results can converge quickly. Thus, we propose an entropy-based model to evaluate the informativeness of HITs. Furthermore, we find that selecting HITs has factorial complexity and the optimization function is non-linear, thus, we propose an efficient approximation algorithm with a bounded error. (2) Uncertainty Management: Crowdsourced answers can be inaccurate. To address this issue, we provide effective solutions in three common scenarios of crowdsourcing: (a) the answer and the confidence of each worker are available; (b) the confidence of each worker and the voting score for each HIT are available; (c) only the answer of each worker is available. To verify the effectiveness of the MaC framework, we built a hybrid Machine-Crowd system and tested it on three real-world applications - data fusion, information extraction and pattern recognition. The experimental results verified the effectiveness and the applicability of our framework.
Generalized Bias-Variance Evaluation of TREC Participated Systems
Authors: Peng Zhang, Linxue Hao, Dawei Song, Jun Wang, Yuexian Hou, Bin Hu
Venue: CIKM
Year: 2014

Recent research has shown that the improvement of mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation of both effectiveness and stability are often based on a baseline model, which could be weak or biased. In addition, the effectiveness-stability tradeoff has not been systematically or quantitatively evaluated over TREC participated systems. The above two problems, to some extent, limit our awareness of such tradeoff and its impact on developing future IR models. In this paper, motivated by a recently proposed bias-variance based evaluation, we adopt a strong and unbiased "baseline", which is a virtual target model constructed by the best performance (for each query) among all the participated systems in a retrieval task. We also propose generalized bias-variance metrics, based on which a systematic and quantitative evaluation of the effectiveness-stability tradeoff is carried out over the participated systems in the TREC Ad-hoc Track (1993-1999) and Web Track (2010-2012). We observe a clear effectiveness-stability tradeoff, with a trend of becoming more obvious in more recent years. This implies that when we pursue more effective IR systems over years, the stability has become problematic and could have been largely overlooked.
NCR: A Scalable Network-Based Approach to Co-Ranking in Question-and-Answer Sites
Authors: Jingyuan Zhang, Xiangnan Kong, Roger Jie Luo, Yi Chang, Philip S. Yu
Venue: CIKM
Year: 2014

Question-and-answer (Q&A) websites, such as Yahoo! Answers, Stack Overflow and Quora, have become a popular and powerful platform for Web users to share knowledge on a wide range of subjects. This has led to a rapidly growing volume of information and the consequent challenge of readily identifying high quality objects (questions, answers and users) in Q&A sites. Exploring the interdependent relationships among different types of objects can help find high quality objects in Q&A sites more accurately. In this paper, we specifically focus on the ranking problem of co-ranking questions, answers and users in a Q&A website. By studying the tightly connected relationships between Q&A objects, we can gain useful insights toward solving the co-ranking problem. However, co-ranking multiple objects in Q&A sites is a challenging task: a) With the large volumes of data in Q&A sites, it is important to design a model that can scale well; b) The large-scale Q&A data makes extracting supervised information very expensive. In order to address these issues, we propose an unsupervised Network-based Co-Ranking framework (NCR) to rank multiple objects in Q&A sites. Empirical studies on real-world Yahoo! Answers datasets demonstrate the effectiveness and the efficiency of the proposed NCR method.
Exploit Latent Dirichlet Allocation for One-Class Collaborative Filtering
Authors: Haijun Zhang, Zhoujun Li, Yan Chen, Xiaoming Zhang, Senzhang Wang
Venue: CIKM
Year: 2014

Previous work studied one-class collaborative filtering (OCCF) problems including pointwise methods, pairwise methods, and content-based methods. The fundamental assumptions made on these approaches are roughly the same. They regard all missing values as negative. However, this is unreasonable since the missing values actually are the mixture of negative and positive examples. A user does not give a positive feedback on an item probably only because she/he is unaware of the item, but in fact, she/he is fond of it. Furthermore, content-based methods, e.g. collaborative topic regression (CTR), usually require textual content information of items. This cannot be satisfied in some cases. In this paper, we exploit latent Dirichlet allocation (LDA) model on OCCF problem. It assumes missing values unknown and only models the observed data, and it also does not need content information of items. In our model items are regarded as words and users are considered as documents and the user-item feedback matrix denotes the corpus. Experimental results show that our proposed method outperforms the previous methods on various ranking-oriented evaluation metrics.
PatentDom: Analyzing Patent Relationships on Multi-View Patent Graphs
Authors: Longhui Zhang, Lei Li, Tao Li, Dingding Wang
Venue: CIKM
Year: 2014

The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grasp the linkage between different technologies in order to better understand the technical trend. The availability of huge volumes of granted patent documents provides a reasonable basis for analyzing the relationships between technologies. In this paper, we propose a unified framework, named PatentDom, to identify important patents related to key techniques from a large number of patent documents. The framework integrates different types of patent information, including patent content, citations of patents, and temporal relations, and provides a concise yet comprehensive technology summary. The identified key patents enable a variety of patent-related analytical applications, e.g., outlining the technology evolution of a particular domain, tracing a given technique to prior technologies, and mining the technical connection of two given patent documents. Empirical analysis and extensive case studies on a collection of US patent documents demonstrate the efficacy of our proposed framework.
WiiCluster: a Platform for Wikipedia Infobox Generation
Authors: Kezun Zhang, Yanghua Xiao, Hanghang Tong, Haixun Wang, Wei Wang
Venue: CIKM
Year: 2014

Wikipedia has become one of the best sources for creating and sharing a massive volume of human knowledge. Much effort has been devoted to generating and enriching the structured data by automatic information extraction from unstructured text in Wikipedia. Most, if not all, of the existing work share the same paradigm, that is, starting with information extraction over the unstructured text data, followed by supervised machine learning. Although remarkable progresses have been made, this paradigm has its own limitations in terms of effectiveness, scalability as well as the high labeling cost.