OpenRaiser/Intern-Atlas huggingface dataset:
Mining coherent anomaly collections on web data
Authors: Hanbo Dai, Feida Zhu, Ee-Peng Lim, HweeHwa Pang
Venue: CIKM
Year: 2012

The recent boom of weblogs and social media has attached increasing importance to the identification of suspicious users with unusual behavior, such as spammers or fraudulent reviewers. A typical spamming strategy is to employ multiple dummy accounts to collectively promote a target, be it a URL or a product. Consequently, these suspicious accounts exhibit certain coherent anomalous behavior identifiable as a collection. In this paper, we propose the concept of Coherent Anomaly Collection (CAC) to capture this kind of collections, and put forward an efficient algorithm to simultaneously find the top-K disjoint CACs together with their anomalous behavior patterns. Compared with existing approaches, our new algorithm can find disjoint anomaly collections with coherent extreme behavior without having to specify either their number or sizes. Results on real Twitter data show that our approach discovers meaningful and informative hashtag spammer groups of various sizes which are hard to detect by clustering-based methods.
Domain dependent query reformulation for web search
Authors: Van Dang, Giridhar Kumaran, Adam D. Troy
Venue: CIKM
Year: 2012

Query reformulation has been studied as a domain independent task. Existing work attempts to expand a query or substitute its terms with the same set of candidates regardless of the domain of this query. Since terms might be semantically related in one domain but not in others, it is more effective to provide candidates for queries with respect to their domain. This paper demonstrates the advantage of this domain dependent query reformulation approach, which learns its candidates, using a standard technique, for each domain from a separate sample of data derived automatically from a generic query log. Our results show that our approach statistically significantly outperforms the domain independent approach, which learns to reformulate from the same log using the same technique, on a large query set consisting of both health and commerce queries. Our results have very practical interpretation: while building different reformulation systems to handle queries from different domains does not require additional manual effort, it provides substantially better retrieval effectiveness than having a single system handling all queries. Additionally, we show that leveraging domain specific manually labelled data leads to further improvement.
Exploiting 5W annotations for opinion tracking
Authors: Amitava Das, Björn Gambäck
Venue: CIKM
Year: 2012

A system performing sentiment analysis needs some prior knowledge which can be acquired, for example, by manual annotation only or by expanding a human-annotated sentiment lexicon by automatic means. However, users are seldom interested in all sentimental aspects at once, but rather look for opinion changes of some person ("Who") during some time period ("When") and depending upon "What", "Where" or "Why" something happened. The paper describes the work necessary to obtain and utilize such 5W annotations.
Efficient jaccard-based diversity analysis of large document collections
Authors: Fan Deng, Stefan Siersdorfer, Sergej Zerr
Venue: CIKM
Year: 2012

We propose two efficient algorithms for exploring topic diversity in large document corpora such as user generated content on the social web, bibliographic data, or other web repositories. Analyzing diversity is useful for obtaining insights into knowledge evolution, trends, periodicities, and topic heterogeneity of such collections. Calculating diversity statistics requires averaging over the similarity of all object pairs, which, for large corpora, is prohibitive from a computational point of view. Our proposed algorithms overcome the quadratic complexity of the average pair-wise similarity computation, and allow for constant time (depending on dataset properties) or linear time approximation with probabilistic guarantees. We show examples of diversity-based studies on large samples from corpora such as the social photo sharing site Flickr, the DBLP bibliography, and US Census data.
Primates: a privacy management system for social networks
Authors: Imen Ben Dhia, Talel Abdessalem, Mauro Sozio
Venue: CIKM
Year: 2012

While online social networks (OSN) present unprecedented opportunities for sharing information and multimedia content among users, they raise major privacy issues as users could often access personal or confidential data of other users. Most social networks provide some basic access control policies, which however seem to be very limited given the diversity of user relationships in the current social networks (e.g. friend, acquaintance, son) as well as the needs of social network users who might want to express sophisticated access control policies (e.g. invite all children of my colleagues to my child's birthday party). In this demonstration proposal, we present Primates a privacy management system for social networks. Primates allows users to specify access control rules for their resources and enforces access control over all shared resources. The set of users who are allowed to access a given resource is defined by a set of constraints on the paths connecting the owner of a resource to its requester in the social graph. We demonstrate the accuracy of our access control model and the scalability of our system.
What is happening right now ... that interests me?: online topic discovery and recommendation in twitter
Authors: Ernesto Diaz-Aviles, Lucas Drumond, Zeno Gantner, Lars Schmidt-Thieme, Wolfgang Nejdl
Venue: CIKM
Year: 2012

Users engaged in the Social Web increasingly rely upon continuous streams of Twitter messages (tweets) for real-time access to information and fresh knowledge about current affairs. However, given the deluge of tweets, it is a challenge for individuals to find relevant and appropriately ranked information. We propose to address this knowledge management problem by going beyond the general perspective of information finding in Twitter, that asks: "What is happening right now?", towards an individual user perspective, and ask: "What is interesting to me right now?" In this paper, we consider collaborative filtering as an online ranking problem and present RMFO, a method that creates, in real-time, user-specific rankings for a set of tweets based on individual preferences that are inferred from the user's past system interactions. Experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's global trend and Weighted Regularized Matrix Factorization (WRMF), a highly competitive state-of-the-art Collaborative Filtering technique, demonstrating the efficacy of our approach.
The walls have ears: optimize sharing for visibility and privacy in online social networks
Authors: Thang N. Dinh, Yilin Shen, My T. Thai
Venue: CIKM
Year: 2012

With a rapid expansion of online social networks (OSNs), millions of users are tweeting and sharing their personal status daily without being aware of where that information eventually travels to. Likewise, with a huge magnitude of data available on OSNs, it poses a substantial challenge to track how a piece of information leaks to specific targets. In this paper, we study the problem of smartly sharing information to control the propagation of sensitive information in OSNs.
Leaving so soon?: understanding and predicting web search abandonment rationales
Authors: Abdigani Diriye, Ryen White, Georg Buscher, Susan T. Dumais
Venue: CIKM
Year: 2012

Users of search engines often abandon their searches. Despite the high frequency of Web search abandonment and its importance to Web search engines, little is known about why searchers abandon beyond that it can be for good or bad reasons. In this paper, we ex-tend previous work by studying search abandonment using both a retrospective survey and an in-situ method that captures aban-donment rationales at abandonment time. We show that although satisfaction is a common motivator for abandonment, one-in-five abandonment instances does not relate to satisfaction. We also studied the automatic prediction of the underlying reason for ob-served abandonment. We used features of the query and the results, interaction with the result page (e.g., cursor movements, scrolling, clicks), and the full search session. We show that our classifiers can learn to accurately predict the reasons for observed search abandonment. Such accurate predictions help search providers estimate user satisfaction for queries without clicks, affording a more complete understanding of search engine performance.
A general framework to encode heterogeneous information sources for contextual pattern mining
Authors: Weishan Dong, Wei Fan, Lei Shi, Changjin Zhou, Xifeng Yan
Venue: CIKM
Year: 2012

Traditional pattern mining methods usually work on single data sources. However, in practice, there are often multiple and heterogeneous information sources. They collectively provide contextual information not available in any single source alone describing the same set of objects, and are useful for discovering hidden contextual patterns. One important challenge is to provide a general methodology to mine contextual patterns easily and efficiently. In this paper, we propose a general framework to encode contextual information from multiple sources into a coherent representation---Contextual Information Graph (CIG). The complexity of the encoding scheme is linear in both time and space. More importantly, CIG can be handled by any single-source pattern mining algorithms that accept taxonomies without any modification. We demonstrate by three applications of the contextual association rule, sequence and graph mining, that contextual patterns providing rich and insightful knowledge can be easily discovered by the proposed framework. It enables Contextual Pattern Mining (CPM) by reusing single-source methods, and is easy to deploy and use in real-world systems.
Lonomics Atlas: a tool to explore interconnected ionomic, genomic and environmental data
Authors: Eduard C. Dragut, Mourad Ouzzani, Amgad Madkour, Mohamed Nabeel, Peter Baker, David E. Salt
Venue: CIKM
Year: 2012

Ionomics Atlas facilitates access, analysis and interpretation of an existing large-scale heterogeneous dataset consisting of ionomic (elemental composition of an organism), genetic (heritable changes in the DNA of an organism) and geographic information (geographic location, altitude, climate, soil properties, etc). Ionomics Atlas allows connections to be made between the genetic regulation of the ionome of plant populations and their landscape distribution, allowing scientists to investigate the role of natural ionomic variation in adaptation of populations to varied environmental conditions in the landscape. The goal of the Ionomics Atlas is twofold: (1) to allow both novice and expert users to easily access and explore layers of interconnected ionomic, genomic and environmental data; and (2) to facilitate hypothesis generation and testing by proving direct querying and browsing of the data as well as different display modes of the results.
Exploring and analyzing documents with OLAP
Authors: Grzegorz Drzadzewski, Frank Wm. Tompa
Venue: CIKM
Year: 2012

When faced with a document collection of substantial size, it is difficult for users to explore and analyze the information contained in it. Tagging has been used to improve the organization of documents in a collection, but it has various limitations. We propose to improve the analysis and exploration of tagged document collections by organizing the documents into clusters and allowing users to perform online analytical processing on the clusters. However, supporting OLAP on clusters of documents poses various challenges that need to be addressed. These challenges include providing efficient representations for cluster centroids and document positions inside the clusters, dealing with overlapping clusters, efficient and accurate aggregation of clusters, providing functionality for helping users find representative documents for a cluster, and determining the strength of relationship between clusters.
Click patterns: an empirical representation of complex query intents
Authors: Huizhong Duan, Emre Kiciman, ChengXiang Zhai
Venue: CIKM
Year: 2012

Understanding users' search intents is critical component of modern search engines. A key limitation made by most query log analyses is the assumption that each clicked web result represents one unique intent. However, there are many search tasks, such as comparison shopping or in-depth research, where a user's intent is to explore many documents. In these cases, the assumption of a one-to-one correspondence between clicked documents and user intent breaks down.
Graph-based collective classification for tweets
Authors: Yajuan Duan, Furu Wei, Ming Zhou, Heung-Yeung Shum
Venue: CIKM
Year: 2012

In this paper, we address the problem of classifying tweets into topical categories. Because of the short, noisy and ambiguous nature of tweets, we propose to collectively conduct the classification by exploiting the context information (i.e. related tweets) other than individually as in conventional text classification methods. In particular, we augment the content-based representation of text with tweets sharing same #hashtag or URL, which results in a tweet graph. We then formulate the tweet classification task under a graph optimization framework. We investigate three popular approaches, namely, Loopy Belief Propagation (LBP), Relaxation Labeling (RL), and Iterative Classification Algorithm (ICA). Extensive experiment results show that the graph-based tweet classification approach remarkably improves the performance, while the ICA model with relationship of sharing the same #hashtag gives the best result on separate tweet graph.
Maximizing revenue from strategic recommendations under decaying trust
Authors: Paul Dütting, Monika Henzinger, Ingmar Weber
Venue: CIKM
Year: 2012

Suppose your sole interest in recommending a product to me is to maximize the amount paid to you by the seller for a sequence of recommendations. How should you recommend optimally if I become more inclined to ignore you with each irrelevant recommendation you make? Finding an answer to this question is a key challenge in all forms of marketing that rely on and explore social ties; ranging from personal recommendations to viral marketing. We prove that even if the recommendee regains her initial trust on each successful recommendation, the expected revenue the recommender can make over an infinite period due to payments by the seller is bounded. This can only be overcome when the recommendee also incrementally regains trust during periods without any recommendation. Here, we see a connection to "banner blindness," suggesting that showing fewer ads can lead to a higher long-term revenue.
Data filtering in humor generation: comparative analysis of hit rate and co-occurrence rankings as a method to choose usable pun candidates
Authors: Pawel Dybala, Rafal Rzepka, Kenji Araki, Kohichi Sayama
Venue: CIKM
Year: 2012

In this paper we propose a method of filtering excessive amount of textual data acquired from the Internet. In our research on pun generation in Japanese we experienced problems with extensively long data processing time, caused by the amount of phonetic candidates generated (i.e. phrases that can be used to generate actual puns) by our system. Simple, naive approach in which we take into considerations only phrases with the highest occurrence in the Internet, can effect in deletion of those candidates that are actually usable. Thus, we propose a data filtering method in which we compare two Internet-based rankings: a co-occurrence ranking and a hit rate ranking, and select only candidates which occupy the same or similar positions in these rankings. In this work we analyze the effects of such data reduction, considering 1 cases: when the candidates are on exactly the same positions in both rankings, and when their positions differ by 1, 2, 3 and 4. The analysis is conducted on data acquired by comparing pun candidates generated by the system (and filtered with our method) with phrases that were actually used in puns created by humans. The results show that the proposed method can be used to filter excessive amounts of textual data acquired from the Internet.
Why query annotations may help in providing accurate public health information
Authors: Ann-Marie Eklund
Venue: CIKM
Year: 2012

The mobility era has highlighted the importance of information providers to better understand the needs of the information seekers, even when the user interaction becomes less verbose. This is even more important in contexts like health care, where an understanding of the users' language and intentions is critical to provide accurate health information. To study the used language we utilize an annotation-based method to analyze if the seekers' language deviates from the health care professionals', and the one found in medical terminologies and information sources. By an analysis of the use of a major public Swedish health information portal we present, in our opinion, a surprising overlap in the language used by laymen and professionals, thereby supporting the use of terminology-based annotation to facilitate improved information retrieval in this setting.
An analysis of how ensembles of collective classifiers improve predictions in graphs
Authors: Hoda Eldardiry, Jennifer Neville
Venue: CIKM
Year: 2012

We present a theoretical analysis framework that shows how ensembles of collective classifiers can improve predictions for graph data. We show how collective ensemble classification reduces errors due to variance in learning and more interestingly inference. We also present an empirical framework that includes various ensemble techniques for classifying relational data using collective inference. The methods span single- and multiple-graph network approaches, and are tested on both synthetic and real world classification tasks. Our experimental results, supported by our theoretical justifications, confirm that ensemble algorithms that explicitly focus on both learning and inference processes and aim at reducing errors associated with both, are the best performers.
Exploration of monte-carlo based probabilistic query processing in uncertain graphs
Authors: Tobias Emrich, Hans-Peter Kriegel, Johannes Niedermayer, Matthias Renz, André Suhartha, Andreas Züfle
Venue: CIKM
Year: 2012

This demo presents a framework for running probabilistic graph queries on uncertain graphs and visualizing their results. The framework supports the most common uncertainty model for uncertain graphs, i.e. existential uncertainty for the edges of the graph. A large variety of meaningful graph queries are supported, such as shortest path, range, kN, reverse kN, reachability and various aggregation queries. Since the problem of exact probability computation according to possible world semantics is in #P-Time for many combinations of model and query, and since ignoring uncertainty (e.g. by using expectations only) will yield counterintuitive and hard to interpret results, our framework uses an optimized version of Monte-Carlo sampling to estimate the results which allows us not only to perform queries that conform to possible world semantics but also to sample only parts of a graph relevant for a given query. The main strength of this framework is the visualization combined with statistic hypothesis tests, which gives the user not only the estimated result of a query, but also an indication of how significant and reliable these results are. The aim of this demonstration is to give an intuition that a sampling based approach to probabilistic graphs is viable, and that the estimated results quickly converge even for very large graphs. A video demonstrating our framework can be downloaded at http://www.dbs.ifi.lmu.de/Publikationen/videos/PGraph.html
Real-time aggregate monitoring with differential privacy
Authors: Liyue Fan, Li Xiong
Venue: CIKM
Year: 2012

Sharing real-time aggregate statistics of private data has given much benefit to the public to perform data mining for understanding important phenomena, such as Influenza outbreaks and traffic congestion. However, releasing time-series data with standard differential privacy mechanism has limited utility due to high correlation between data values. We propose FAST, an adaptive system to release real-time aggregate statistics under differential privacy with improved utility. To minimize overall privacy cost, FAST adaptively samples long time-series according to detected data dynamics. To improve the accuracy of data release per time stamp, filtering is used to predict data values at non-sampling points and to estimate true values from noisy observations at sampling points. Our experiments with three real data sets confirm that FAST improves the accuracy of time-series release and has excellent performance even under very small privacy cost.
Maximum margin clustering on evolutionary data
Authors: Xuhui Fan, Lin Zhu, Longbing Cao, Xia Cui, Yew-Soon Ong
Venue: CIKM
Year: 2012

Evolutionary data, such as topic changing blogs and evolving trading behaviors in capital market, is widely seen in business and social applications. The time factor and intrinsic change embedded in evolutionary data greatly challenge evolutionary clustering. To incorporate the time factor, existing methods mainly regard the evolutionary clustering problem as a linear combination of snapshot cost and temporal cost, and reflect the time factor through the temporal cost. It still faces accuracy and scalability challenge though promising results gotten. This paper proposes a novel evolutionary clustering approach, evolutionary maximum margin clustering (e-MMC), to cluster large-scale evolutionary data from the maximum margin perspective. e-MMC incorporates two frameworks: Data Integration from the data changing perspective and Model Integration corresponding to model adjustment to tackle the time factor and change, with an adaptive label allocation mechanism. Three e-MMC clustering algorithms are proposed based on the two frameworks. Extensive experiments are performed on synthetic data, UCI data and real-world blog data, which confirm that e-MMC outperforms the state-of-the-art clustering algorithms in terms of accuracy, computational cost and scalability. It shows that e-MMC is particularly suitable for clustering large-scale evolving data.
A latent pairwise preference learning approach for recommendation from implicit feedback
Authors: Yi Fang, Luo Si
Venue: CIKM
Year: 2012

Most of the current recommender systems heavily rely on explicit user feedback such as ratings on items to model users' interests. However, in many applications, it is very hard to collect the explicit feedback, while implicit feedback such as user clicks may be more available. Furthermore, it is often more suitable for many recommender systems to address a ranking problem than a rating predicting problem. This paper proposes a latent pairwise preference learning (LPPL) approach for recommendation with implicit feedback. LPPL directly models user preferences with respect to a set of items rather than the rating scores on individual items, which are modeled with a set of features by analyzing clickthrough data available in many real-world recommender systems. The LPPL approach models both the latent variables of group structure of users and the pairwise preferences simultaneously. We conduct experiments on the testbed from a real-world recommender system and demonstrate that the proposed approach can effectively improve the recommendation performance against several baseline algorithms.