OpenRaiser/Intern-Atlas huggingface dataset:
A Study of Query Length Heuristics in Information Retrieval
Authors: Yuanhua Lv
Venue: CIKM
Year: 2015

Query length has generally been regarded as a query-specific constant that does not affect document ranking. In this paper, we reveal that query length actually interacts with term frequency (TF) normalization, a key component of all effective retrieval models. Specifically, the longer the query is, the smaller the TF decay speed should be. In order to study the impact of query length, we present a desirable formal constraint to capture the heuristic of query length for retrieval. Our constraint analysis shows that current state-of-the-art retrieval functions, including BM25 and language models, fail to satisfy the constraint, and that, in order to solve this problem, the TF normalization component in a retrieval function should be adapted to query length. As an application, we develop a simple regression algorithm to adapt BM25 to query length, and demonstrate its effectiveness on several representative TREC collections.
Dynamic Resource Management In a Massively Parallel Stream Processing Engine
Authors: Kasper Grud Skat Madsen, Yongluan Zhou
Venue: CIKM
Year: 2015

The emerging interest in Massively Parallel Stream Processing Engines (MPSPEs), which are able to process long-standing computations over data streams with ever-growing velocity at a large-scale cluster, calls for efficient dynamic resource management techniques to avoid any waste of resources and/or excessive processing latency. In this paper, we propose an approach to integrate dynamic resource management with passive fault-tolerance mechanisms in a MPSPE so that we can harvest the checkpoints prepared for failure recovery to enhance the efficiency of dynamic load migrations. To maximize the opportunity of reusing checkpoints for fast load migration, we formally define a checkpoint allocation problem and provide a pragmatic algorithm to solve it. We implement all the proposed techniques on top of Apache Storm, an open-source MPSPE, and conduct extensive experiments using a real dataset to examine various aspects of our techniques. The results show that our techniques can greatly improve the efficiency of dynamic resource reconfiguration without imposing significant overhead or latency to the normal job execution.
Fraud Transaction Recognition: A Money Flow Network Approach
Authors: Renxin Mao, Zhao Li, Jinhua Fu
Venue: CIKM
Year: 2015

In this paper, we provide some insights into analysis of fraud transaction recognition on Alipay's Money Flow Network. We first show that the Money Flow Network follows a power-law distribution on daily, monthly or yearly basis, based on which we propose a new approach of fraud transaction recognition on the Money Flow Network from two perspectives. First, the Collapse Network is identified by the discovery that fraud transaction requires a huge amount of active controlled 'zombie' accounts, which are always intentionally manipulated by fraudulent online sellers, and the collapse of the Money Flow Network emerges due to their economic inactivity; Second, we define the Activation Forest that leads to the recognition of the controlled 'zombies' even no sooner than they enter into Alipay's ecosphere. These two networks are fully explored from the perspective of detecting 'zombies', and several key features have been adopted into anti-fraud recognition. Experimental results show that our strategy is capable of effectively identifying fraud transactions on the Money Flow Network with the accuracy as high as 99.88%.
Identifying Top-k Consistent News-Casters on Twitter
Authors: Sahisnu Mazumder, Sameep Mehta, Dhaval Patel
Venue: CIKM
Year: 2015

News-casters are Twitter users who periodically pick up interesting news from online news media and spread it to their followers' network. Existing works on Twitter user analysis have only analysed a pre-defined set of users for user modeling, influence analysis and news recommendation. The problem of identifying prominent, trustworthy and consistent news-casters is unaddressed so far. In this paper, we present a framework, NCFinder, to discover top-k consistent news-casters directly from Twitter. NCFinder uses news headlines published in online news sources to periodically collect authentic news-tweets and processes them to discover news-casters, news sources and news concepts. Next, NCFinder builds a tripartite graph among news-casters, news source and news concepts and employs HITS algorithm on it to score the news-casters on daily basis. The daily score profiles of the news-casters collected over a time-period are then used to infer top-$k$ consistent news-casters. We run NCFinder from 11th Nov. to 24th Nov., 2014 and discover top-100 consistent news-casters and their profile information.
Aggregation of Crowdsourced Ordinal Assessments and Integration with Learning to Rank: A Latent Trait Model
Authors: Pavel Metrikov, Virgil Pavlu, Javed A. Aslam
Venue: CIKM
Year: 2015

Existing approaches used for training and evaluating search engines often rely on crowdsourced assessments of document relevance with respect to a user query. To use such assessments for either evaluation or learning, we propose a new framework for the inference of true document relevance from crowdsourced data---one simpler than previous approaches and achieving better performance. For each assessor, we model assessor quality and bias in the form of Gaussian distributed class conditionals of relevance grades. For each document, we model true relevance and difficulty as continuous variables. We estimate all parameters from crowdsourced data, demonstrating better inference of relevance as well as realistic models for both documents and assessors.
Learning Task Grouping using Supervised Task Space Partitioning in Lifelong Multitask Learning
Authors: Meenakshi Mishra, Jun Huan
Venue: CIKM
Year: 2015

Lifelong multitask learning is a multitask learning framework in which a learning agent faces the tasks that need to be learnt in an online manner. Lifelong multitask learning framework may be applied to a variety of applications such as image annotation, robotics, automated machines etc, and hence, may prove to be a highly promising direction for further investigation. However, the lifelong learning framework comes with its own baggage of challenges. The biggest challenge is the fact that the characteristics of the future tasks which might be encountered by the learning agents are entirely unknown. If all the tasks are assumed to be related, there may be a risk of training from unrelated task resulting in negative transfer of information. To overcome this problem, both batch and online multitask learning algorithms learn task relationships. However, due to the unknown nature of the future tasks, learning the task relationships is also difficult in lifelong multitask learning. In this paper, we propose learning functions to model the task relationships as it is computationally cheaper in an online setting. More specifically, we learn partition functions in the task space to divide the tasks into cluster. Our major contribution is to present a global formulation to learn both the task partitions and the parameters. We provide a supervised learning framework to estimate both the partition function and the model. The current method has been implemented and compared against other leading lifelong learning algorithms using several real world datasets, and we show that the current method has a superior performance.
What Is a Network Community?: A Novel Quality Function and Detection Algorithms
Authors: Atsushi Miyauchi, Yasushi Kawase
Venue: CIKM
Year: 2015

In this study, we introduce a novel quality function for a network community, which we refer to as the communitude. The communitude has a strong statistical background. Specifically, it measures the Z-score of a subset of vertices S with respect to the fraction of the number of edges within the subgraph induced by S. Due to the null model of a random graph used in the definition, our quality function focuses not only on the inside of the subgraph but also on the cut edges, unlike some quality functions for extracting dense subgraphs. To evaluate the detection ability of our quality function, we address the communitude maximization problem and its variants for realistic scenarios. For the problems, we propose a two-phase heuristic algorithm together with some modified versions. In the first phase, it repeatedly removes the vertex with the smallest degree, and then obtains the subgraph with maximum communitude over the iterations. In the second phase, the algorithm improves the obtained solution using a simple local search heuristic. This algorithm runs in linear time when the number of iterations is fixed to a constant; thus, it is applicable to massive graphs. Computational experiments using both synthetic graphs and real-world networks demonstrate the validity and reliability of the proposed quality function and algorithms.
Pooled Evaluation Over Query Variations: Users are as Diverse as Systems
Authors: Alistair Moffat, Falk Scholer, Paul Thomas, Peter Bailey
Venue: CIKM
Year: 2015

Evaluation of information retrieval systems with test collections makes use of a suite of fixed resources: a document corpus; a set of topics; and associated judgments of the relevance of each document to each topic. With large modern collections, exhaustive judging is not feasible. Therefore an approach called pooling is typically used where, for example, the documents to be judged can be determined by taking the union of all documents returned in the top positions of the answer lists returned by a range of systems. Conventionally, pooling uses system variations to provide diverse documents to be judged for a topic; different user queries are not considered. We explore the ramifications of user query variability on pooling, and demonstrate that conventional test collections do not cover this source of variation. The effect of user query variation on the size of the judging pool is just as strong as the effect of retrieval system variation. We conclude that user query variation should be incorporated early in test collection construction, and cannot be considered effectively post hoc.
UCUI'15: The 1st International Workshop on Understanding the City with Urban Informatics
Authors: Yashar Moshfeghi, Iadh Ounis, Craig Macdonald, Joemon M. Jose, Peter Triantafillou, Mark Livingston, Piyushimita Thakuriah
Venue: CIKM
Year: 2015

Urban Informatics aims to exploit the large quantities of information produced by modern cities in order to gain insights into how they function. These insights lay the foundation for improving the lives of citizens, by improving the efficacy and efficiency of public services, and satisfying complex information needs arising within this context. The goal of the workshop is to provide a multidisciplinary forum which brings together researchers in Big Data (BD), Information Retrieval (IR), Data Mining, and Urban Studies, to explore novel solutions to the numerous theoretical, practical and ethical challenges arising in this context. These include difficulties in collecting city data, creating data management infrastructures, and providing new effective and efficient information access techniques to as many users as possible in the context of a smart city. To foster the development of new BD and IR approaches in Urban Informatics, the workshop makes available a representative dataset of city data, including Internet-based visual (Flickr) and textual (Tweets and News) media collections. The workshop provides enormous opportunities for data scientists who wish to understand the complexities of working with city data, conduct innovative research within Urban Informatics, and build a long-term community in this emerging research area.
Lifespan-based Partitioning of Index Structures for Time-travel Text Search
Authors: Animesh Nandi, Suriya Subramanian, Sriram Lakshminarasimhan, Prasad M. Deshpande, Sriram Raghavan
Venue: CIKM
Year: 2015

Time-travel text search over a temporally evolving document collection is useful in various applications. Supporting a wide range of query classes demanded by these applications require different index layouts optimized for their respective query access patterns. The problem we tackle is how to efficiently handle different query classes using the same index layout.
Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media
Authors: Tom De Nies, Io Taxidou, Anastasia Dimou, Ruben Verborgh, Peter M. Fischer, Erik Mannens, Rik Van de Walle
Venue: CIKM
Year: 2015

In order to assess the trustworthiness of information on social media, a consumer needs to understand where this information comes from, and which processes were involved in its creation. The entities, agents and activities involved in the creation of a piece of information are referred to as its provenance, which was standardized by W3C PROV. However, current social media APIs cannot always capture the full lineage of every message, leaving the consumer with incomplete or missing provenance, which is crucial for judging the trust it carries. Therefore in this paper, we propose an approach to reconstruct the provenance of messages on social media on multiple levels. To obtain a fine-grained level of provenance, we use an approach from prior work to reconstruct information cascades with high certainty, and map them to PROV using the PROV-SAID extension for social media. To obtain a coarse-grained level of provenance, we adapt our similarity-based, fuzzy provenance reconstruction approach -- previously applied on news. We illustrate the power of the combination by providing the reconstructed provenance of a limited social media dataset gathered during the 2012 Olympics, for which we were able to reconstruct a significant amount of previously unidentified connections.
Implementing Query Completeness Reasoning
Authors: Werner Nutt, Sergey Paramonov, Ognjen Savkovic
Venue: CIKM
Year: 2015

Data completeness is commonly regarded as one of the key aspects of data quality. With this paper we make two main contributions: (i) we develop techniques to reason about the completeness of a query answer over a partially complete database, taking into account constraints that hold over the database, and (ii) we implement them by an encoding into logic programming paradigms. As constraints we consider primary and foreign keys as well as finite domain constraints. In this way we can identify more situations in which a query is complete than was possible with previous work. For each combination of constraints, we establish characterizations of the completeness reasoning and we show how to translate them into logic programs. As a proof of concept we ran our encodings against test cases that capture characteristics of a real-world scenario.
The Influence of Pre-processing on the Estimation of Readability of Web Documents
Authors: João Rafael De Moura Palotti, Guido Zuccon, Allan Hanbury
Venue: CIKM
Year: 2015

This paper investigates the effect that text pre-processing approaches have on the estimation of the readability of web pages. Readability has been highlighted as an important aspect of web search result personalisation in previous work. The most widely used text readability measures rely on surface level characteristics of text, such as the length of words and sentences. We demonstrate that different tools for extracting text from web pages lead to very different estimations of readability. This has an important implication for search engines because search result personalisation strategies that consider users reading ability may fail if incorrect text readability estimations are computed.
A Fast k-Nearest Neighbor Search Using Query-Specific Signature Selection
Authors: Youngki Park, Heasoo Hwang, Sang-goo Lee
Venue: CIKM
Year: 2015

k-nearest neighbor (k-NN) search aims at finding k points nearest to a query point in a given dataset. k-NN search is important in various applications, but it becomes extremely expensive in a high-dimensional large dataset. To address this performance issue, locality-sensitive hashing (LSH) is suggested as a method of probabilistic dimension reduction while preserving the relative distances between points. However, the performance of existing LSH schemes is still inconsistent, requiring a large amount of search time in some datasets while the k-NN approximation accuracy is low. In this paper, we target on improving the performance of k-NN search and achieving a consistent k-NN search that performs well in various datasets. In this regard, we propose a novel LSH scheme called Signature Selection LSH (S2LSH). First, we generate a highly diversified signature pool containing signature regions of various sizes and shapes. Then, for a given query point, we rank signature regions of the query and select points in the highly ranked signature regions as k-NN candidates of the query. Extensive experiments show that our approach consistently outperforms the state-of-the-art LSH schemes.
The Role of Query Sessions in Interpreting Compound Noun Phrases
Authors: Marius Pasca
Venue: CIKM
Year: 2015

The meaning of compound noun phrases can be approximated in the form of lexical interpretations extracted from text. The interpretations hint at the role that modifiers play relative to heads within the noun phrases. In a study examining the role of query sessions in explaining compound noun phrases, candidate interpretations of compound noun phrases are extracted from pairs of queries that belong to the same query session. Experimental results over multiple evaluation sets of noun phrases show a higher accuracy of the interpretations when extracted from query sessions rather than from individual queries.
Core-Sets For Canonical Correlation Analysis
Authors: Saurabh Paul
Venue: CIKM
Year: 2015

Canonical Correlation Analysis (CCA) is a technique that finds how "similar" are the subspaces that are spanned by the columns of two different matrices A έℜ(of size m-x-n) and B έℜ(of size m-x-l). CCA measures similarity by means of the cosines of the so-called principal angles between the two subspaces. Those values are also known as canonical correlations of the matrix pair (A,B). In this work, we consider the over-constrained case where the number of rows is greater than the number of columns (m > max(n,l)). We study the problem of constructing "core-sets" for CCA. A core-set is a subset of rows from A and the corresponding subset of rows from B - denoted by Â and B, respectively. A "good" core-set is a subset of rows such that the canonical correlations of the core-set (Â, B) are "close" to the canonical correlations of the original matrix pair (A, B). There is a natural tradeoff between the core-set size and the approximation accuracy of a core-set. We present two algorithms namely, single-set spectral sparsification and leverage-score sampling, which find core-sets with additive-error guarantees to canonical correlations.
DeepCamera: A Unified Framework for Recognizing Places-of-Interest based on Deep ConvNets
Authors: Pai Peng, Hongxiang Chen, Lidan Shou, Ke Chen, Gang Chen, Chang Xu
Venue: CIKM
Year: 2015

In this work, we present a novel project called DeepCamera(DC) for recognizing places-of-interest(POI) with smartphones. Our framework is based on deep convolutional neural networks(ConvNets) which are currently state-of-the-art solutions to vision recognition tasks such as our mission. We propose a novel ConvNet by introducing a new layer called "spatial layer" which captures spatial knowledge from a geographic view. As a result, both spatial and visual knowledge contribute to generating a hybrid probability distribution over all possible POI candidates. Furthermore, we compress multiple trained deep ConvNets into one single shallow net called "shNet" which achieves competitive performance with ensemble methods. Our preliminary experiments conducted on real-world dataset have shown promising POI recognition results.
Central Topic Model for Event-oriented Topics Mining in Microblog Stream
Authors: Min Peng, Jiahui Zhu, Xuhui Li, Jiajia Huang, Hua Wang, Yanchun Zhang
Venue: CIKM
Year: 2015

To date, data generates and arrives in the form of stream to propagate discussions of public events in microblog services. Discovering event-oriented topics from the stream will lead to a better understanding of the change of public concern. However, as the massive scale of the data stream, traditional static topic models, such as LDA, are no longer fit for topic detection and tracking tasks. In this paper, we propose a central topic model (CenTM), where a Multi-view Clustering algorithm with Two-phase Random Walk (MC-TRW) is devised to aggregate the LDA's latent topics into central topics. Furthermore, we leverage the aggregation of central topics alternately with MC-TRW and sequential topic inference to improve the scalability in the stream fashion, so as to derive the dynamic central topic model (DCenTM). Specifically, our model is able to uncover the intrinsic characteristics of the central topics and predict the trend of their intensity along a life cycle. Experimental results demonstrate that the proposed central topic model is event-oriented and of high generalization, it therefore can dispose the topic trend prediction effectively and precisely in massive data stream.
Atypical Queries in eCommerce
Authors: Neeraj Pradhan, Vinay Deolalikar, Kang Li
Venue: CIKM
Year: 2015

Understanding how specific, ambiguous, or broad the intent of a search query is, across all users of the system, is important in improving search relevance in eCommerce. There is scant literature on such a structural characterization of queries in eCommerce. In this paper, we use query-click log data to address the problem of identifying "atypical queries": these are queries that are extremal in terms of specificity, ambiguity, or breadth of intent. We isolate three components of atypicality: geometric, statistical, and topological. We demonstrate, using query-click logs at Groupon, that certain combinations of these properties render a query atypical, and discuss how search analysts treat such queries differently. Our work is being used to improve search relevance at Groupon.
Entity and Aspect Extraction for Organizing News Comments
Authors: Radityo Eko Prasojo, Mouna Kacimi, Werner Nutt
Venue: CIKM
Year: 2015

News websites give their users the opportunity to participate in discussions about published articles, by writing comments. Typically, these comments are unstructured making it hard to understand the flow of user discussions. Thus, there is a need for organizing comments to help users to (1) gain more insights about news topics, and (2) have an easy access to comments that trigger their interests. In this work, we address the above problem by organizing comments around the entities and the aspects they discuss. More specifically, we propose an approach for entity and aspect extraction from user comments through the following contributions. First, we extend traditional Named-Entity Recognition approaches, using coreference resolution and external knowledge bases, to detect more occurrences of entities in comments. Second, we exploit part-of-speech tag, dependency tag, and lexical databases to extract explicit and implicit aspects around discussed entities. Third, we evaluate our entity and aspect extraction approach, on manually annotated data, showing that it highly increases precision and recall compared to baseline approaches.