OpenRaiser/Intern-Atlas huggingface dataset:
MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction
Authors: Sarah Masud Preum, John A. Stankovic, Yanjun Qi
Venue: CIKM
Year: 2015

The primary objective of this research is to develop a simple and interpretable predictive framework to perform temporal modeling of individual user's behavior traits based on each person's past observed traits/behavior. Individual-level human behavior patterns are possibly influenced by various temporal features (e.g., lag, cycle) and vary across temporal scales (e.g., hour of the day, day of the week). Most of the existing forecasting models do not capture such multi-scale adaptive regularity of human behavior or lack interpretability due to relying on hidden variables. Hence, we build a multi-scale adaptive personalized (MAPer) model that quantifies the effect of both lag and behavior cycle for predicting future behavior. MAper includes a novel basis vector to adaptively learn behavior patterns and capture the variation of lag and cycle across multi-scale temporal contexts. We also extend MAPer to capture the interaction among multiple behaviors to improve the prediction performance.
Topic Modeling in Semantic Space with Keywords
Authors: Xiaojia Pu, Rong Jin, Gangshan Wu, Dingyi Han, Gui-Rong Xue
Venue: CIKM
Year: 2015

A common and convenient approach for user to describe his information need is to provide a set of keywords. Therefore, the technique to understand the need becomes crucial. In this paper, for the information need about a topic or category, we propose a novel method called TDCS(Topic Distilling with Compressive Sensing) for explicit and accurate modeling the topic implied by several keywords. The task is transformed as a topic reconstruction problem in the semantic space with a reasonable intuition that the topic is sparse in the semantic space. The latent semantic space could be mined from documents via unsupervised methods, e.g. LSI. Compressive sensing is leveraged to obtain a sparse representation from only a few keywords. In order to make the distilled topic more robust, an iterative learning approach is adopted. The experiment results show the effectiveness of our method. Moreover, with only a few semantic concepts remained for the topic, our method is efficient for subsequent text mining tasks.
Structured Sparse Regression for Recommender Systems
Authors: Mingjie Qian, Liangjie Hong, Yue Shi, Suju Rajan
Venue: CIKM
Year: 2015

Feature-based collaborative filtering models, such as state-of-the-art factorization machines and regression-based latent factor models, rarely consider features' structural information, ignoring the heterogeneity of inter-type and intra-type relationships. Naïvely treating all feature pairs equally would potentially deteriorate the overall recommendation performance. In addition, human prior knowledge and other hierarchical or graphical structures are often available for some features, e.g., the country-state-city hierarchy for geographic features and the topical taxonomy for article features. It is a challenge to utilize the prior knowledge to further boost performance of state-of-the-art models. In this paper we employ rich features from both user and item sides to enhance latent factors learnt from interaction data, uncovering hidden structures from features' relationships and learning sparse pairwise and tree structural connections among features. Our framework borrows the modeling strengh from both structural sparsity modeling and latent factor models. Experiments on a real-world large-scale recommendation data set demonstrated that the proposed model outperforms several strong state-of-the-art baselines.
NWSearch 2015: International Workshop on Novel Web Search Interfaces and Systems
Authors: Davood Rafiei, Katsumi Tanaka
Venue: CIKM
Year: 2015

Held for the first time in conjunction with the ACM International Conference on Information and Knowledge Management (CIKM), NWSearch 2015 aims to bring together researchers, developers and practitioners who are interested in pushing the search boundary on the Web and exploring more novel forms of searches, interfaces, task formulations, and result organizations and presentations. In particular, the workshop seeks to identify some of the problems and challenges facing the development of such tools and interfaces and to flourish new ideas and findings that can shape or influence future research directions and developments. The workshop organizers solicited contributions that would fall within the large spectrum of human-computer interaction in one extreme and system production and development in the other extreme.
Identifying Top-k Structural Hole Spanners in Large-Scale Social Networks
Authors: Mojtaba Rezvani, Weifa Liang, Wenzheng Xu, Chengfei Liu
Venue: CIKM
Year: 2015

Recent studies have shown that in social networks, users who bridge different communities, known as structural hole spanners, have great potentials to acquire available resources from these communities and gain access to multiple sources of information flow. Structural hole spanners are crucial in many applications such as community detections, diffusion controls, and viral marketing. In spite of their importance, not much attention has been paid to them. Particularly, how to characterize the structural hole spanner properties and how to devise efficient yet scalable algorithms to find them are fundamental issues. In this paper, we formulate the problem as the top-k structural hole spanner problem. Specifically, we first provide a generic model to measure the quality of structural hole spanners, by exploring their properties, and show that the problem is NP-hard. We then devise efficient and scalable algorithms, by exploiting the bounded inverse closeness centralities of vertices and making use of articulation points of the network. We finally evaluate the performance of the proposed algorithms through extensive experiments on real and synthetic datasets, and validate the effectiveness of the proposed model. Our experimental results demonstrate that the proposed model can capture the characteristics of structural hole spanners accurately, and the proposed algorithms are very promising.
Analyzing Document Intensive Business Processes using Ontology
Authors: Suman Roychoudhury, Vinay Kulkarni, Nikhil Bellarykar
Venue: CIKM
Year: 2015

Knowledge is manifested in an enterprise in various forms ranging from unstructured operational data, to structured information like programs, as well as relational data stored in databases to semi-structured information stored in XML files. This information embodies the core of an enterprise knowledge base and analyzing the knowledge base can result in intelligent decision making. In order to realize this goal we begin with representing and analyzing unstructured knowledge present in an enterprise. In particular, this paper presents a real life example of a document intensive business process (International Trade) and attempts to model and analyze the process in a formal way. Typically, the information contained in a document intensive business process is of operational nature and requires extensive manual verification, which is both time consuming and error prone. Therefore, this research aims to eliminate such exhaustive manual verification by constructing a knowledge base in the form of ontology and apply suitable rule based reasoners to automate the verification process.
A Probabilistic Framework for Temporal User Modeling on Microblogs
Authors: Jitao Sang, Dongyuan Lu, Changsheng Xu
Venue: CIKM
Year: 2015

In social media, users have contributed enormous behavior data online which can be leveraged for user modeling and conduct personalized services. Temporal user modeling, which incorporates the timestamp of these behavior data and understands users' interest evolution, have attracted attention recently. With the recognition that user interests are vulnerable to transient events, many current temporal user modeling solutions propose to first identify the transient events and then consider the identified events into user behavior modeling. In this work, in the context of microblogs, we propose a unified probabilistic framework to simultaneously model the process of transient event detection and temporal user tweeting. The outputs of the framework include: (1) one long-term topic space spanning over general categories, (2) one short-term topic space for each time interval corresponding to the transient events, and (3) users' interest distributions over the long- and short-term topic spaces. Qualitative and quantitative experimental evaluation are conducted on a large-scale Twitter dataset, with more than 2 million users and 0.3 billion tweets. The promising results demonstrate the advantage of the proposed topic models.
Semi-Automated Exploration of Data Warehouses
Authors: Thibault Sellam, Emmanuel Müller, Martin L. Kersten
Venue: CIKM
Year: 2015

Exploratory data analysis tries to discover novel dependencies and unexpected patterns in large databases. Traditionally, this process is manual and hypothesis-driven. However, analysts can come short of patience and imagination. In this paper, we introduce Claude, a hypothesis generator for data warehouses. Claude follows a 2-step approach: (1) It detects interesting views, by exploiting non-linear statistical dependencies between the dimensions and the measure. (2) To explain its findings, it detects local patterns in these views and describes them with SQL queries. Technically, we derive a model of interestingness from fundamental information theory. To exploit this model, we present aggressive approximations and heuristics, allowing Claude to be fast and more accurate than state-of-art view selection algorithms.
Towards Scale-out Capability on Social Graphs
Authors: Haichuan Shang, Xiang Zhao, R. Uday Kiran, Masaru Kitsuregawa
Venue: CIKM
Year: 2015

The development of cloud storage and computing has facilitated the rise of various big data applications. As a representative high performance computing (HPC) workload, graph processing is becoming a part of cloud computing. However, scalable computing on large graphs is still dominated by HPC solutions, which require high performance all-to-all collective operations over torus (or mesh) networking. Implementing those torus-based algorithms on commodity clusters, e.g., cloud computing infrastructures, can result in great latency due to inefficient communication. Moreover, designing a highly scalable system for large social graphs, is far from being trivial, as intrinsic features of social graphs, e.g., degree skewness and lacking of locality, often profoundly limit the extent of parallelism.
F1: Accelerating the Optimization of Aggregate Continuous Queries
Authors: Anatoli U. Shein, Panos K. Chrysanthis, Alexandros Labrinidis
Venue: CIKM
Year: 2015

Data Stream Management Systems performing on-line analytics rely on the efficient execution of large numbers of Aggregate Continuous Queries (ACQs). The state-of-the-art WeaveShare optimizer uses the Weavability concept in order to selectively combine ACQs for partial aggregation and produce high quality execution plans. However, WeaveShare does not scale well with the number of ACQs. In this paper we propose a novel closed formula, F1, that accelerates Weavability calculations, and thus allows WeaveShare to achieve exceptional scalability in systems with heavy workloads. In general, F1 can reduce the computation time of any technique that combines partial aggregations within composite slides of multiple ACQs. We theoretically analyze the Bit Set approach currently used by WeaveShare and show that F1 is superior in both time and space complexities. We show that F1 performs 1062 times less operations compared to Bit Set to produce the same execution plan for the same input. We experimentally show that F1 executes up to 60,000 times faster and can handle 1,000,000 ACQs in a setting where the limit for the current technique is 550.
Transductive Domain Adaptation with Affinity Learning
Authors: Le Shu, Longin Jan Latecki
Venue: CIKM
Year: 2015

We study the problem of domain adaptation, which aims to adapt the classifiers trained on a labeled source domain to an unlabeled target domain. We propose a novel method to solve domain adaptation task in a transductive setting. The proposed method bridges the distribution gap between source domain and target domain through affinity learning. It exploits the existence of a subset of data points in target domain which distribute similarly to the data points in the source domain. These data points act as the bridge that facilitates the data similarities propagation across domains. We also propose to control the relative importance of intra- and inter-domain similarities to boost the similarity propagation. In our approach, we first construct the similarity matrix which encodes both the intra- and inter-domain similarities. We then learn the true similarities among data points in joint manifold using graph diffusion.
Who With Whom And How?: Extracting Large Social Networks Using Search Engines
Authors: Stefan Siersdorfer, Philipp Kemkes, Hanno Ackermann, Sergej Zerr
Venue: CIKM
Year: 2015

Social network analysis is leveraged in a variety of applications such as identifying influential entities, detecting communities with special interests, and determining the flow of information and innovations. However, existing approaches for extracting social networks from unstructured Web content do not scale well and are only feasible for small graphs. In this paper, we introduce novel methodologies for query-based search engine mining, enabling efficient extraction of social networks from large amounts of Web data. To this end, we use patterns in phrase queries for retrieving entity connections, and employ a bootstrapping approach for iteratively expanding the pattern set. Our experimental evaluation in different domains demonstrates that our algorithms provide high quality results and allow for scalable and efficient construction of social graphs.
Bottom-up Faceted Search: Creating Search Neighbourhoods with Datacube Cells
Authors: Mark Sifer
Venue: CIKM
Year: 2015

Browsing a collection can start with a keyword search. A user visits a library, performs a keyword search to find a few books of interest; finding their location in the library. Then they go to these locations; the corresponding bookshelves, where they do not just retrieve the found books, but rather they start browsing the nearby books; the books which have a similar Dewey classification. This paper extends this approach to curated corpora that contain items or documents that have been classified in multiple dimensions (facets), where each dimension classification may be a hierarchy. In particular (i) a technique for determining near items based on OLAP datacube cells and (ii) user interfaces that support browsing of near items are presented.
The Role Of Citation Context In Predicting Long-Term Citation Profiles: An Experimental Study Based On A Massive Bibliographic Text Dataset
Authors: Mayank Singh, Vikas Patidar, Suhansanu Kumar, Tanmoy Chakraborty, Animesh Mukherjee, Pawan Goyal
Venue: CIKM
Year: 2015

The impact and significance of a scientific publication is measured mostly by the number of citations it accumulates over the years. Early prediction of the citation profile of research articles is a significant as well as challenging problem. In this paper, we argue that features gathered from the citation contexts of the research papers can be very relevant for citation prediction. Analyzing a massive dataset of nearly 1.5 million computer science articles and more than 26 million citation contexts, we show that average countX (number of times a paper is cited within the same article) and average citeWords (number of words within the citation context) discriminate between various citation ranges as well as citation categories. We use these features in a stratified learning framework for future citation prediction. Experimental results show that the proposed model significantly outperforms the existing citation prediction models by a margin of 8-10% on an average under various experimental settings. Specifically, the features derived from the citation context help in predicting long-term citation behavior.
Personalized Recommendation Meets Your Next Favorite
Authors: Qiang Song, Jian Cheng, Ting Yuan, Hanqing Lu
Venue: CIKM
Year: 2015

A comprehensive understanding of user's item selection behavior is not only essential to many scientific disciplines, but also has a profound business impact on online recommendation. Recent researches have discovered that user's favorites can be divided into 2 categories: long-term and short-term. User's item selection behavior is a mixed decision of her long and short-term favorites. In this paper, we propose a unified model, namely States Transition pAir-wise Ranking Model (STAR), to address users' favorites mining for sequential-set recommendation. Our method utilizes a transition graph for collaborative filtering that accounts for mining user's short-term favorites, jointed with a generative topic model for expressing user's long-term favorites. Furthermore, a user's specific prior is introduced into our unified model for better modeling personalization. Technically, we develop a pair-wise ranking loss function for parameters learning. Empirically, we measure the effectiveness of our method using two real-world datasets and the results show that our method outperforms state-of-the-art methods.
Mining Brokers in Dynamic Social Networks
Authors: Chonggang Song, Wynne Hsu, Mong-Li Lee
Venue: CIKM
Year: 2015

The theory of brokerage in sociology suggests if contacts between two parties are enabled through a third party, the latter occupies a strategic position of controlling information flows. Such individuals are called brokers and they play a key role in disseminating information. However, there is no systematic approach to identify brokers in online social networks. In this paper, we formally define the problem of detecting top-$k$ brokers given a social network and show that it is NP-hard. We develop a heuristic algorithm to find these brokers based on the weak tie theory. In order to handle the dynamic nature of online social networks, we design incremental algorithms: WeakTie-Local for unidirectional networks and WeakTie-Bi for bidirectional networks. We use two real world datasets, DBLP and Twitter, to evaluate the proposed methods. We also demonstrate how the detected brokers are useful in diffusing information across communities and propagating tweets to reach more distinct users.
DTMBIO 2015: International Workshop on Data and Text Mining in Biomedical Informatics
Authors: Min Song, Doheon Lee, Karin Verspoor
Venue: CIKM
Year: 2015

Held each year in conjunction with one of the largest data management conferences, CIKM, the Ninth ACM International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO'15) is organized to bring together researchers interested in development and application of cutting-edge data management and analysis methods with a specific focus on applications in biology and medicine. The purpose of DTMBIO is to foster discussions regarding the state-of-the-art applications of data and text mining on biomedical research problems. DTMBIO'15 will help scientists understand emerging trends and opportunities in the evolving area of informatics related techniques and problems in the context of biomedical research.
Recommending Short-lived Dynamic Packages for Golf Booking Services
Authors: Robin M. E. Swezey, Young-joo Chung
Venue: CIKM
Year: 2015

We introduce an approach to recommending short-lived dynamic packages for golf booking services. Two challenges are addressed in this work. The first is the short life of the items, which puts the system in a state of a permanent cold start. The second is the uninformative nature of the package attributes, which makes clustering or figuring latent packages challenging. Although such settings are fairly pervasive, they have not been studied in traditional recommendation research, and there is thus a call for original approaches for recommender systems. In this paper, we introduce a hybrid method that leverages user analysis and its relation to the packages, as well as package pricing and environmental analysis, and traditional collaborative filtering. The proposed approach achieved appreciable improvement in precision compared with baselines
Slow Search: Improving Information Retrieval Using Human Assistance
Authors: Jaime Teevan
Venue: CIKM
Year: 2015

We live in a world where the pace of everything from communication to transportation is getting faster. In recent years a number of "slow movements" have emerged that advocate for reducing speed in exchange for increasing quality, including the slow food movement, slow parenting, slow travel, and even slow science. We propose the concept of "slow search," where search engines use additional time to provide a higher quality search experience than is possible given conventional time constraints. While additional time can be used to identify relevant results within the existing search engine framework, it can also be used to create new search artifacts and enable previously unimaginable user experiences. This talk will focus on how search engines can make use of additional time to employ a resource that is inherently slow: other people. Using crowdsourcing and friendsourcing, it will highlight opportunities for search systems to support new search experiences with high quality result content that takes time to identify.
Large-Scale Analysis of Dynamics of Choice Among Discrete Alternatives
Authors: Andrew Tomkins
Venue: CIKM
Year: 2015

The online world is rife with scenarios in which a user must select one from a finite set of alternatives: which movie to watch, which song to play, which camera to order, which website to visit. There is a long history of study of these types of questions in economics, machine learning, marketing, and psychology. However, historically the study of choice was limited to relatively modest data scales. Today, we have access to large-scale datasets providing insights into the choices of large populations of users faced with a wide variety of sets of alternatives. From such data, we are beginning to develop more detailed models of how users weigh alternatives and make selections.
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events
Authors: Tuan Tran, Claudia Niederée, Nattiya Kanhabua, Ujwal Gadiraju, Avishek Anand
Venue: CIKM
Year: 2015

Long-running, high-impact events such as the Boston Marathon bombing often develop through many stages and involve a large number of entities in their unfolding. Timeline summarization of an event by key sentences eases story digestion, but does not distinguish between what a user remembers and what she might want to re-check. In this work, we present a novel approach for timeline summarization of high-impact events, which uses entities instead of sentences for summarizing the event at each individual point in time. Such entity summaries can serve as both (1) important memory cues in a retrospective event consideration and (2) pointers for personalized event exploration. In order to automatically create such summaries, it is crucial to identify the "right" entities for inclusion. We propose to learn a ranking function for entities, with a dynamically adapted trade-off between the in-document salience of entities and the informativeness of entities across documents, i.e., the level of new information associated with an entity for a time point under consideration. Furthermore, for capturing collective attention for an entity we use an innovative soft labeling approach based on Wikipedia. Our experiments on a real large news datasets confirm the effectiveness of the proposed methods.