OpenRaiser/Intern-Atlas huggingface dataset:
Entity-centric document filtering: boosting feature mapping through meta-features
Authors: Mianwei Zhou, Kevin Chen-Chuan Chang
Venue: CIKM
Year: 2013

This paper studies the entity-centric document filtering task -- given an entity represented by its identification page (e.g., an Wikpedia page), how to correctly identify its relevant documents. In particular, we are interested in learning an entity-centric document filter based on a small number of training entities, and the filter can predict document relevance for a large set of unseen entities at query time. Towards characterizing the relevance of a document, the problem boils down to learning keyword importance for the query entities. Since the same keyword will have very different importance for different entities, we abstract the entity-centric document filtering problem as a transfer learning problem, and the challenge becomes how to appropriately transfer the keyword importance learned from training entities to query entities. Based on the insight that keywords sharing some similar "properties" should have similar importance for their respective entities, we propose a novel concept of meta-feature to map keywords from different entities. To realize the idea of meta-feature-based feature mapping, we develop and contrast two different models, LinearMapping and BoostMapping. Experiments on three different datasets confirm the effectiveness of our proposed models, which show significant improvement compared with four state-of-the-art baseline methods.
Towards faster and better retrieval models for question search
Authors: Guangyou Zhou, Yubo Chen, Daojian Zeng, Jun Zhao
Venue: CIKM
Year: 2013

Community question answering (cQA) has become an important service due to the popularity of cQA archives on the web. This paper is concerned with the problem of question search. Question search in cQA aims to find the historical questions that are semantically equivalent or similar to the queried questions. In this paper, we propose a faster and better retrieval model for question search by leveraging user chosen category. After introducing the question category, we can filter certain amount of irrelevant historical questions under a wide range of leaf categories. Experimental results conducted on real cQA data set demonstrate that the proposed techniques are more effective and efficient than a variety of baseline methods.
On the reliability and intuitiveness of aggregated search metrics
Authors: Ke Zhou, Mounia Lalmas, Tetsuya Sakai, Ronan Cummins, Joemon M. Jose
Venue: CIKM
Year: 2013

Aggregating search results from a variety of diverse verticals such as news, images, videos and Wikipedia into a single interface is a popular web search presentation paradigm. Although several aggregated search (AS) metrics have been proposed to evaluate AS result pages, their properties remain poorly understood. In this paper, we compare the properties of existing AS metrics under the assumptions that (1) queries may have multiple preferred verticals; (2) the likelihood of each vertical preference is available; and (3) the topical relevance assessments of results returned from each vertical is available. We compare a wide range of AS metrics on two test collections. Our main criteria of comparison are (1) discriminative power, which represents the reliability of a metric in comparing the performance of systems, and (2) intuitiveness, which represents how well a metric captures the various key aspects to be measured (i.e. various aspects of a user's perception of AS result pages). Our study shows that the AS metrics that capture key AS components (e.g., vertical selection) have several advantages over other metrics. This work sheds new lights on the further developments and applications of AS metrics.
Top-down keyword query processing on XML data
Authors: Junfeng Zhou, Xingmin Zhao, Wei Wang, Ziyang Chen, Jeffrey Xu Yu
Venue: CIKM
Year: 2013

Efficiently answering XML keyword queries has attracted much research effort in the last decade. One key factors resulting in the inefficiency of existing methods are the common-ancestor-repetition (CAR) and visiting-useless-nodes (VUN) problems. In this paper, we propose a generic top-down processing strategy to answer a given keyword query w.r.t. LCA/SLCA/ELCA semantics. By top-down, we mean that we visit all common ancestor (CA) nodes in a depth-first, left-to-right order, thus avoid the CAR problem; by generic, we mean that our method is independent of the labeling schemes and query semantics. We show that the satisfiability of a node v w.r.t. the given semantics can be determined by v's child nodes, based on which our methods avoid the VUN problem. We propose two algorithms that are based on either traditional inverted lists or our newly proposed LLists to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.
A pattern-based selective recrawling approach for object-level vertical search
Authors: Yaqian Zhou, Qi Zhang, Xuanjing Huang, Lide Wu
Venue: CIKM
Year: 2013

Traditional recrawling methods learn navigation patterns in order to crawl related web pages. However, they cannot remove the redundancy found on the web, especially at the object level. To deal with this problem, we propose a new hypertext resource discovery method, called ``selective recrawling'' for object-level vertical search applications. The goal of selective recrawling is to automatically generate URL patterns, then select those pages that have the widest coverage, and least irrelevance and redundancy relative to a pre-defined vertical domain. This method only requires a few seed objects and can select the set of URL patterns that covers the greatest number of objects. The selected set can continue to be used for some time to recrawl web pages and can be renewed periodically. This leads to significant savings in hardware and network resources.
Ranking fraud detection for mobile apps: a holistic view
Authors: Hengshu Zhu, Hui Xiong, Yong Ge, Enhong Chen
Venue: CIKM
Year: 2013

Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App develops to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we investigate two types of evidences, ranking based evidences and rating based evidences, by modeling Apps' ranking and rating behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the Apple's App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.
Predicting user activity level in social networks
Authors: Yin Zhu, Erheng Zhong, Sinno Jialin Pan, Xiao Wang, Minzhe Zhou, Qiang Yang
Venue: CIKM
Year: 2013

The study of users' social behaviors has gained much research attention since the advent of various social media such as Facebook, Renren and Twitter. A major kind of applications is to predict a user's future activities based on his/her historical social behaviors. In this paper, we focus on a fundamental task: to predict a user's future activity levels in a social network, e.g. weekly activeness, active or inactive. This problem is closely related to Social Customer Relationship Management (Social CRM). Compared to traditional CRM, the three properties: user diversity, social influence, and dynamic nature of social networks, raise new challenges and opportunities to Social CRM. Firstly, the user diversity property implies that a global predictive model may not be precise for all users. On the other hand, historical data of individual users are too sparse to build precisely personalized models. Secondly, the social influence property suggests that relationships between users can be embedded to further boost prediction results on individual users. Finally, the dynamical nature of social networks means that users' behaviors may keep changing over time. To address these challenges, we develop a personalized and social regularized time-decay model for user activity level prediction. Experiments on the social media Renren validate the effectiveness of our proposed model compared with some baselines including traditional supervised learning methods and node classification methods in social networks.
A belief propagation approach for detecting shilling attacks in collaborative filtering
Authors: Jun Zou, Faramarz Fekri
Venue: CIKM
Year: 2013

Recommender systems have been widely used in e-commerce websites to suggest items that meet users' preferences. Collaborative filtering, which is the most popular recommendation algorithm, is vulnerable to shilling attacks, where a group of spam users collaborate to manipulate the recommendations. Several attack detection algorithms have been developed to detect spam users and remove them from the system. However, the existing algorithms focus mostly on rating patterns of users. In this paper, we develop a probabilistic inference framework that further exploits the target items for attack detection. In addition, the user features can also be conveniently incorporated in this framework. We utilize the Belief Propagation (BP) algorithm to perform inference efficiently. Experimental results verify that the proposed algorithm significantly improves detection performance as the number of target items increases.
DFD: Efficient Functional Dependency Discovery
Authors: Ziawasch Abedjan, Patrick Schulze, Felix Naumann
Venue: CIKM
Year: 2014

The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets.
Similarity Search using Concept Graphs
Authors: Rakesh Agrawal, Sreenivas Gollapudi, Anitha Kannan, Krishnaram Kenthapadi
Venue: CIKM
Year: 2014

The rapid proliferation of hand-held devices has led to the development of rich, interactive and immersive applications, such as e-readers for electronic books. These applications motivate retrieval systems that can implicitly satisfy any information need of the reader by exploiting the context of the user's interactions. Such retrieval systems differ from traditional search engines in that the queries constructed using the context are typically complex objects (including the document and its structure).
LocWeb'14 - 4th International Workshop on Location and the Web: CIKM 2014 Workshop Summary
Authors: Dirk Ahlers, Erik Wilde, Bruno Martins
Venue: CIKM
Year: 2014

The LocWeb 2014 workshop continues a successful workshop series at the intersection of geospatial search, information management, and Web architecture with a focus towards location-aware information access. The workshop reflects a multitude of fields that demand and utilize location features, featuring presentations that look at the topic of location on the Web from an interdisciplinary perspective, including new approaches dealing with or utilizing geospatial information.
Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering
Authors: Ibrahim M. Alabdulmohsin, Xin Gao, Xiangliang Zhang
Venue: CIKM
Year: 2014

Many classification algorithms have been successfully deployed in security-sensitive applications including spam filters and intrusion detection systems. Under such adversarial environments, adversaries can generate exploratory attacks against the defender such as evasion and reverse engineering. In this paper, we discuss why reverse engineering attacks can be carried out quite efficiently against fixed classifiers, and investigate the use of randomization as a suitable strategy for mitigating their risk. In particular, we derive a semidefinite programming (SDP) formulation for learning a distribution of classifiers subject to the constraint that any single classifier picked at random from such distribution provides reliable predictions with a high probability. We analyze the tradeoff between variance of the distribution and its predictive accuracy, and establish that one can almost always incorporate randomization with large variance without incurring a loss in accuracy. In other words, the conventional approach of using a fixed classifier in adversarial environments is generally Pareto suboptimal. Finally, we validate such conclusions on both synthetic and real-world classification problems.
Simple Arabic Stemmer
Authors: Mohammed Algarni, Brent Martin, Tim Bell, Kourosh Neshatian
Venue: CIKM
Year: 2014

We propose a root stemmer for the Modern Standard Arabic (MSA) language in an attempt to enhance the performance of Arabic Information Retrieval (AIR). The new Simple Arabic Stemmer (SAS) is based on the Quran morphology, since the Quran was a key source for the derivation of Arabic morphological rules. The stemmer is developed by decomposing all of the Quran words and studying their internal morphological structure including the roots, the patterns, and the affixes employed in the generation process. We were able to construct a relatively small lexicon capable of finding the root for most of the MSA vocabulary. Using the TREC corpus and queries, we test our approach against two well-known root stemmers, Khoja and Sebawai. The results show that SAS gives an improvement in terms of precision.
Increasing the Responsiveness of Recommended Expert Collaborators for Online Open Projects
Authors: Mohammad Y. Allaho, Wang-Chien Lee
Venue: CIKM
Year: 2014

We consider the experts recommendation problem for open collaborative projects in large-scale Open Source Software (OSS) communities. In large-scale online community, recommending expert collaborators to a project coordinator or lead developer has two prominent challenges: (i) the "cold shoulder"' problem, which is the lack of interest from the experts to collaborate and share their skills, and (ii) the "cold start" problem, which is an issue with community members who has scarce data history. In this paper, we consider the Degree of Knowledge (DoK) which imposes the knowledge of the skills factor, and the Social Relative Importance (SRI) which imposes the social distance factor to tackle the aforementioned challenges. We propose four DoK models and integrate them with three SRI methods under our proposed Expert Ranking (ER) framework to rank the candidate expert collaborators based on their likelihood of collaborating in response to a query formulated by the social network of a query initiator and certain required skills to a project/task. We evaluate our proposal using a dataset collected from Github.com, which is one of the most fast-growing, large-scale online OSS community. In addition, we test the models under different data scarcity levels. The experiment shows promising results of recommending expert collaborators who tend to make real collaborations to projects.
Hotspot Detection in a Service-Oriented Architecture
Authors: Pranay Anchuri, Roshan Sumbaly, Sam Shah
Venue: CIKM
Year: 2014

Large-scale websites are predominantly built as a service-oriented architecture. Here, services are specialized for a certain task, run on multiple machines, and communicate with each other to serve a user's request. Reducing latency and improving the cost to serve is quite important, but optimizing this service call graph is particularly challenging due to the volume of data and the graph's non-uniform and dynamic nature.
Understanding Within-Content Engagement through Pattern Analysis of Mouse Gestures
Authors: Ioannis Arapakis, Mounia Lalmas, George Valkanas
Venue: CIKM
Year: 2014

The availability of large volumes of interaction data and scalable data mining techniques have made possible to study the online behaviour for millions of Web users. Part of the efforts have focused on understanding how users interact and engage with web content. However, the measurement of within-content engagement remains a difficult and unsolved task. This is because of the lack of standardised, well-validated methods for measuring engagement, especially in an online context. To address this gap, we perform a controlled user study where we observe how users respond to online news in the presence or lack of interest. We collect mouse tracking data, which are known to correlate with visual attention, and examine how cursor behaviour can inform user engagement measures. The proposed method does not use any pre-determined concepts to characterise the cursor patterns. We, rather, follow an unsupervised approach and use a large set of features engineered from our data to extract the cursor patterns. Our findings support the connection between gaze and cursor behaviour but also, and more importantly, reveal other dependencies, such as the correlation between cursor activity and experienced affect. Finally, we demonstrate the value of our method by predicting the outcome of online news reading experiences.
Narrow or Broad?: Estimating Subjective Specificity in Exploratory Search
Authors: Kumaripaba Athukorala, Antti Oulasvirta, Dorota Glowacka, Jilles Vreeken, Giulio Jacucci
Venue: CIKM
Year: 2014

Supporting exploratory search is a very challenging problem, not least because of the dynamic nature of the exercise: both the knowledge and interests of the user are subject to constant change. Moreover, whether the results for a query are informative is strongly subjective. What is informative to one user, is too specific for the other; specificity differs between users depending on their intent and accumulated knowledge about the domain.
Machine-Assisted Search Preference Evaluation
Authors: Ahmed Hassan Awadallah, Imed Zitouni
Venue: CIKM
Year: 2014

Information Retrieval systems are traditionally evaluated using the relevance of web pages to individual queries. Other work on IR evaluation has focused on exploring the use of preference judgments over two search result lists. Unlike traditional query-document evaluation, collecting preference judgments over two search result-lists takes the context of documents, and hence takes the interaction between search results, into consideration. Moreover, preference judgments have been shown to produce more accurate results compared to absolute judgment. On the other hand result list preference judgments have very high annotation cost. In this work, we investigate how machine learned models can assist human judges in order to collect reliable result list preference judgments at large scale with lower judgment-cost. We build novel models that can predict user preference automatically. We investigate the effect of different features on the prediction quality. We focus on predicting preferences with high confidence and show that these models can be effectively used to assist human judges resulting in significant reduction in annotation cost.
Hashcube: A Data Structure for Space- and Query-Efficient Skycube Compression
Authors: Kenneth S. Bøgh, Sean Chester, Darius Sidlauskas, Ira Assent
Venue: CIKM
Year: 2014

The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. It is an expensive operator whose query performance can greatly benefit from materialization. However, a skyline can be executed over any subspace of dimensions, and the materialization of all subspace skylines, called the skycube, dramatically multiplies data size. Existing methods for skycube compression sacrifice too much query performance; so, we present a novel hashing- and bitstring-based compressed data structure that supports orders of magnitude faster query performance.
Pulling Conjunctive Query Equivalence out of the Bag
Authors: Stefan Böttcher, Sebastian Link, Lin Zhang
Venue: CIKM
Year: 2014

We present LECQTER, a tool for generating a 'perfect example' database, called exemplar, for a given conjunctive query. Indeed, exemplars separate the given query from any non-equivalent query. Therefore, LECQTER reduces the query equivalence problem to an evaluation of the queries on the exemplar. LECQTER can thus be used for applications ranging from testing coded conjunctive SQL queries to learning how to write sound conjunctive SQL queries, as it provides immediate feedback about the semantic correctness of a query, and not just the correctness of the query answer on some database as, e.g., other SQL tutoring systems. This key novelty of LECQTER relies on the bag semantics of SQL since exemplars do not always exist under set semantics. Detailed experiments show that our construction of exemplars is efficient in practice, and that they can separate a number of non-equivalent user queries that is exponential in the size of the exemplar for the target query. We identify natural parameters to control the time and size of the exemplars constructed. Finally, we offer a solution that overcomes the non-existence of exemplars under set semantics.
VFDS: An Application to Generate Fast Sample Databases
Authors: Teodora Sandra Buda, Thomas Cerqueus, John Murphy, Morten Kristiansen
Venue: CIKM
Year: 2014

Large amounts of data often require expensive and time-consuming analysis. Therefore, highly scalable and efficient techniques are necessary to process, analyze and discover useful information. Database sampling has proven to be a powerful method to surpass these limitations. Using only a sample of the original large database brings the benefit of obtaining useful information faster, at the potential expense of lower accuracy. In this paper, we demonstrate \vfds, a novel fast database sampling system that maintains the referential integrity of the data. The system is developed over the open-source database management system, MySQL. We present various scenarios to demonstrate the effectiveness of VFDS in approximate query answering, sample size, and execution time, on both real and synthetic databases.
Time-sensitive Personalized Query Auto-Completion
Authors: Fei Cai, Shangsong Liang, Maarten de Rijke
Venue: CIKM
Year: 2014

Query auto-completion (QAC) is a prominent feature of modern search engines. It is aimed at saving user's time and enhancing the search experience. Current QAC models mostly rank matching QAC candidates according to their past popularity, i.e., frequency. However, query popularity changes over time and may vary drastically across users. Hence, rankings of QAC candidates should be adjusted accordingly. In previous work time-sensitive QAC models and user-specific QAC models have been developed separately. Both types of QAC model lead to important improvements over models that are neither time-sensitive nor personalized. We propose a hybrid QAC model that considers both of these aspects: time-sensitivity and personalization.