OpenRaiser/Intern-Atlas huggingface dataset:
Seeking provenance of information using social media
Authors: Pritam Gundecha, Zhuo Feng, Huan Liu
Venue: CIKM
Year: 2013

Social media propagates breaking news and disinformation alike fast and on an unsurpassed scale. Because of its democratizing nature, social media users can easily produce, receive, and propagate a piece of information without necessarily providing traceable information. Thus, there are no means for a user to verify the provenance (aka sources or originators) of information. The disinformation can cause tragic consequences to society and individuals. This work aims to take advantage of characteristics of social media to provide a solution to the problem of lacking traceable information. Such knowledge can provide additional context to received information such that a user can assess how much value, trust, and validity should be placed in it. In this paper, we are studying a novel research problem that facilitates the seeking of the provenance of information for a few known recipients (less than 1% of the total recipients) by recovering the paths it has taken from its originators. The proposed methodology exploits easily computable node centralities of a large social media network. The experimental results with Facebook and Twitter datasets show that the proposed mechanism is effective in correctly identifying the additional recipients and seeking the provenance of information.
Graph hashing and factorization for fast graph stream classification
Authors: Ting Guo, Lianhua Chi, Xingquan Zhu
Venue: CIKM
Year: 2013

Graph stream classification concerns building learning models from continuously growing graph data, in which an essential step is to explore subgraph features to represent graphs for effective learning and classification. When representing a graph using subgraph features, all existing methods employ coarse-grained feature representation, which only considers whether or not a subgraph feature appears in the graph. In this paper, we propose a fine-grained graph factorization approach for Fast Graph Stream Classification (FGSC). Our main idea is to find a set of cliques as feature base to represent each graph as a linear combination of the base cliques. To achieve this goal, we decompose each graph into a number of cliques and select discriminative cliques to generate a transfer matrix called Clique Set Matrix (M). By using M as the base for formulating graph factorization, each graph is represented in a vector space with each element denoting the degree of the corresponding subgraph feature related to the graph, so existing supervised learning algorithms can be applied to derive learning models for graph classification.
Understanding the roles of sub-graph features for graph classification: an empirical study perspective
Authors: Ting Guo, Xingquan Zhu
Venue: CIKM
Year: 2013

Graph classification concerns the learning of discriminative models, from structured training data, to classify previously unseen graph samples into specific categories, where the main challenge is to explore structural information in the training data to build classifiers. One of the most common graph classification approaches is to use sub-graph features to convert graphs into instance-feature representations, so generic learning algorithms can be applied to derive learning models. Finding good sub-graph features is regarded as an important task for this type of learning approaches, despite that there is no comprehensive understanding on (1) how effective sub-graph features can be used for graph classification? (2) how many sub-graph features are sufficient for good classification results? (3) does the length of the sub-graph features play major roles for classification? and (4) whether some random sub-graphs can be used for graph representation and classification?
Personalized influence maximization on social networks
Authors: Jing Guo, Peng Zhang, Chuan Zhou, Yanan Cao, Li Guo
Venue: CIKM
Year: 2013

In this paper, we study a new problem on social network influence maximization. The problem is defined as, given a target user $w$, finding the top-k most influential nodes for the user. Different from existing influence maximization works which aim to find a small subset of nodes to maximize the spread of influence over the entire network (i.e., global optima), our problem aims to find a small subset of nodes which can maximize the influence spread to a given target user (i.e., local optima). The solution is critical for personalized services on social networks, where fully understanding of each specific user is essential. Although some global influence maximization models can be narrowed down as the solution, these methods often bias to the target node itself. To this end, in this paper we present a local influence maximization solution. We first provide a random function, with low variance guarantee, to randomly simulate the objective function of local influence maximization. Then, we present efficient algorithms with approximation guarantee. For online social network applications, we also present a scalable approximate algorithm by exploring the local cascade structure of the target user. We test the proposed algorithms on several real-world social networks. Experimental results validate the performance of the proposed algorithms.
Flexible and dynamic compromises for effective recommendations
Authors: Saurabh Gupta, Sutanu Chakraborti
Venue: CIKM
Year: 2013

Conversational Recommendation mimics the kind of dialog that takes between a customer and a shopkeeper involving multiple interactions where the user can give feedback at every interaction as opposed to Single Shot Retrieval, which corresponds to a scheme where the system retrieves a set of items in response to a user query in a single interaction. Compromise refers to a particular user preference which the recommender system failed to satisfy. But in the context of conversational systems, where the user's preferences keep on evolving as she interacts with the system, what constitutes as a compromise for her also keeps on changing. Typically, in Single Shot retrieval, the notion of compromise is characterized by the assignment of a particular feature to a particular dominance group such as MIB (higher value is better) or LIB (lower value is better) and this assignment remains true for all the users who use the system.
Permutation indexing: fast approximate retrieval from large corpora
Authors: Maxim Gurevich, Tamás Sarlós
Venue: CIKM
Year: 2013

Inverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a drawback - query retrieval time is highly variable and grows with the corpus size. In this work we propose an alternative technique, permutation indexing, where retrieval cost is strictly bounded and has only logarithmic dependence on the corpus size. Our approach is based on two novel techniques: (a) partitioning of the term space into overlapping clusters of terms that frequently co-occur in queries, and (b) a data structure for compactly encoding results of all queries composed of terms in a cluster as continuous sequences of document ids. Then, query results are retrieved by fetching few small chunks of these sequences. There is a price though: our encoding is lossy and thus returns approximate result sets. The fraction of the true results returned, recall, is controlled by the level of redundancy. The more space is allocated for the permutation index the higher is the recall. We analyze permutation indexing both theoretically under simplified document and query models, and empirically on a realistic document and query collections. We show that although permutation indexing can not replace traditional retrieval methods, since high recall cannot be guaranteed on all queries, it covers up to 77% of tail queries and can be used to speed up retrieval for these queries.
Locality sensitive hashing for scalable structural classification and clustering of web documents
Authors: Christian Hachenberg, Thomas Gottron
Venue: CIKM
Year: 2013

Web content management systems as well as web front ends to databases usually use mechanisms based on homogeneous templates for generating and populating HTML documents containing structured, semi-structured or plain text data. Wrapper based information extraction techniques leverage such templates as an essential cornerstone of their functionality but rely heavily on the availability of proper training documents based on the specific template. Thus, structural classification and structural clustering of web documents is an important contributing factor to the success of those methods. We introduce a novel technique to support these two tasks: template fingerprints. Template fingerprints are locality sensitive hash values in the form of short sequences of characters which effectively represent the underlying template of a web document. Small changes in the document structure, as they may occur in template based documents, lead to no or only minor variations in the corresponding fingerprint. Based on the fingerprints we introduce a scalable index structure and algorithm for large collections of web documents, which can retrieve structurally similar documents efficiently. The effectiveness of our approach is empirically validated in a classification task on a data set of 13,237 documents based on 50 templates from different domains. The general efficiency and scalability is evaluated in a clustering task on a data set retrieved from the Open Directory Project comprising more than 3.6 million web documents. For both tasks, our template fingerprint approach provides results of high quality and demonstrates a linear runtime of O(n) w.r.t. the number of documents.
Structured data in web search
Authors: Alon Y. Halevy
Venue: CIKM
Year: 2013

For the first time since the emergence of the Web, structured data is playing a key role in search engines and is therefore being collected via a concerted effort. Much of this data is being extracted from the Web, which contains vast quantities of structured data on a variety of domains, such as hobbies, products and reference data. Moreover, the Web provides a platform that encourages publishing more data sets from governments and other public organizations. The Web also supports new data management opportunities, such as effective crisis response, data journalism and crowd-sourcing data sets.
On exploiting content and citations together to compute similarity of scientific papers
Authors: Masoud Reyhani Hamedani, Sang-Wook Kim, Sang-Chul Lee, Dong-Jin Kim
Venue: CIKM
Year: 2013

In computing the similarity of scientific papers, previous text-based and link-based similarity measures look at only a single side of the content and citations. In this paper, we propose a novel approach called SimCC that effectively combines the content and citation information to accurately compute the similarity of scientific papers. Unlike previous approaches, SimCC effectively represents both authority and context of a scientific paper simultaneously in computing similarities. Also, we propose SimCC+A to consider recently-published papers. The effectiveness of our proposed method is demonstrated via extensive experiments on a real-world dataset of scientific papers, with more than 100% improvement in accuracy compared with previous methods.
Expertise retrieval in bibliographic network: a topic dominance learning approach
Authors: Seyyed Hadi Hashemi, Mahmood Neshati, Hamid Beigy
Venue: CIKM
Year: 2013

Expert finding in bibliographic networks has received increased interests in recent years. This task concerns with finding relevant researchers for a given topic. Motivated by the observation that rarely do all coauthors contribute to a paper equally, in this paper, we propose a discriminative method to realize leading authors contributing in a scientific publication. Specifically, we cast the problem of expert finding in a bibliographic network to find leading experts in a research group, which is easier to solve. According to some observations, we recognize three feature groups that can discriminate relevant and irrelevant experts. Experimental results on a real dataset, and an automatically generated one that is gathered from Microsoft academic search show that the proposed model significantly improves the performance of expert finding in terms of all common Information Retrieval evaluation metrics.
Beyond clicks: query reformulation as a predictor of search satisfaction
Authors: Ahmed Hassan Awadallah, Xiaolin Shi, Nick Craswell, Bill Ramsey
Venue: CIKM
Year: 2013

To understand whether a user is satisfied with the current search results, implicit behavior is a useful data source, with clicks being the best-known implicit signal. However, it is possible for a non-clicking user to be satisfied and a clicking user to be dissatisfied. Here we study additional implicit signals based on the relationship between the user's current query and the next query, such as their textual similarity and the inter-query time. Using a large unlabeled dataset, a labeled dataset of queries and a labeled dataset of user tasks, we analyze the relationship between these signals. We identify an easily-implemented rule that indicates dissatisfaction: that a similar query issued within a time interval that is short enough (such as five minutes) implies dissatisfaction. By incorporating additional query-based features in the model, we show that a query-based model (with no click information) can indicate satisfaction more accurately than click-based models. The best model uses both query and click features. In addition, by comparing query sequences in successful tasks and unsuccessful tasks, we observe that search success is an incremental process for successful tasks with multiple queries.
Personalized models of search satisfaction
Authors: Ahmed Hassan Awadallah, Ryen W. White
Venue: CIKM
Year: 2013

Search engines need to model user satisfaction to improve their services. Since it is not practical to request feedback on searchers' perceptions and search outcomes directly from users, search engines must estimate satisfaction from behavioral signals such as query refinement, result clicks, and dwell times. This analysis of behavior in the aggregate leads to the development of global metrics such as satisfied result clickthrough (typically operationalized as result-page clicks with dwell time exceeding a particular threshold) that are then applied to all searchers' behavior to estimate satisfac-tion levels. However, satisfaction is a personal belief and how users behave when they are satisfied can also differ. In this paper we verify that searcher behavior when satisfied and dissatisfied is indeed different among individual searchers along a number of dimensions. As a result, we introduce and evaluate learned models of satisfaction for individual searchers and searcher cohorts. Through experimentation via logs from a large commercial Web search engine, we show that our proposed models can predict search satisfaction more accurately than a global baseline that applies the same satisfaction model across all users. Our findings have implications for the study and application of user satisfaction in search systems.
Online learning from streaming data
Authors: Jeff Hawkins
Venue: CIKM
Year: 2013

High velocity machine-generated data is growing rapidly. To act on this data in real time requires models that learn continuously and discover the temporal patterns in noisy data streams. The brain is also an online learning system that builds models from streaming data. In this talk I will describe recent advances in brain theory and how we have applied those advances to machine-generated streaming data.
How can search interfaces enhance the value of semantic annotations (and vice versa)
Authors: Marti A. Hearst
Venue: CIKM
Year: 2013

The organizers of this workshop have issued a call for help, stating that one challenge in using knowledge and semantic annotations in search is that "standard text search excels at shallow information needs expressed by short keyword queries, and so semantic annotation contributes very little". I suggest that the answer is right there in the question, meaning that those who work with deeper knowledge representations should also try to aid more complex information needs. I will talk about search user interfaces, current and future, that have and hopefully will successfully improve user experience by adding in more understanding of content.
Search using semantic framenet frames as variables
Authors: Karin Friberg Heppin
Venue: CIKM
Year: 2013

This paper discusses the possibilty of using semantic frames as variables as a way to search using concepts rather than words as search keys. It aims to show, by an example, what results it could render, rather than how to do it.
Entity disambiguation in anonymized graphs using graph kernels
Authors: Linus Hermansson, Tommi Kerola, Fredrik Johansson, Vinay Jethava, Devdatt P. Dubhashi
Venue: CIKM
Year: 2013

This paper presents a novel method for entity disambiguation in anonymized graphs using local neighborhood structure. Most existing approaches leverage node information, which might not be available in several contexts due to privacy concerns, or information about the sources of the data. We consider this problem in the supervised setting where we are provided only with a base graph and a set of nodes labelled as ambiguous or unambiguous. We characterize the similarity between two nodes based on their local neighborhood structure using graph kernels; and solve the resulting classification task using SVMs. We give empirical evidence on two real-world datasets, comparing our approach to a state-of-the-art method, highlighting the advantages of our approach. We show that using less information, our method is significantly better in terms of either speed or accuracy or both. We also present extensions of two existing graphs kernels, namely, the direct product kernel and the shortest-path kernel, with significant improvements in accuracy. For the direct product kernel, our extension also provides significant computational benefits. Moreover, we design and implement the algorithms of our method to work in a distributed fashion using the GraphLab framework, ensuring high scalability.
The first workshop on user engagement optimization
Authors: Liangjie Hong, Shuang-Hong Yang
Venue: CIKM
Year: 2013

Online user engagement optimization is key to many Internet business. Several research areas are related to the concept of online user engagement optimization, including machine learning, data mining, information retrieval, recommender systems, online A/B (bucket) testing and psychology. In the past, research efforts in this direction are pursued in separate communities and conferences, yielding potential disconnected and repeated results. In addition, researchers and practitioners are sometimes only exposed to a specific aspect of the topic, which might be incomplete and suboptimal to the whole picture. Here, we organize the first workshop on the topic of online user engagement optimization, explicitly targeting the topic as a whole and bring researchers and practitioners together to foster the field. We invite two leading researchers from industry to give keynote talks about online machine learning and online experimentations. In addition, several invited talks from industry and academic researchers have covered the topics of content personalization, online experimental platforms and recommender systems. Also, six novel submissions are included as short papers in the workshop such that new results are discussed and shared among the workshop.
Topical authority propagation on microblogs
Authors: Juan Hu, Yi Fang, Archana Godavarthy
Venue: CIKM
Year: 2013

With a huge number of active users on microblogs, it becomes increasingly important to identify authoritative users on specific topics. This paper tackles the task of finding authorities on Twitter given any query topic. Although there exists much work on identifying influential users on Twitter, most of them focus on global authority regardless of the topic. We propose a novel Topical Authority Propagation (TAP) model by utilizing the fact that topical authority can be propagated through retweeting, i.e., if a user's tweet on a given topic is retweeted by a topical authority, that user is likely to be an authority on the topic as well. Topical relevance of candidate authorities can be seamlessly integrated into the model. Link analysis algorithms such as PageRank can then be utilized to characterize how topical authority is propagated through retweeting. We conduct a set of experiments on Twitter and demonstrate the effectiveness of the proposed approach.
Learning deep structured semantic models for web search using clickthrough data
Authors: Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, Larry P. Heck
Venue: CIKM
Year: 2013

Latent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails. In this study we strive to develop a series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them. The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data. To make our models applicable to large-scale Web search applications, we also use a technique called word hashing, which is shown to effectively scale up our semantic models to handle large vocabularies which are common in such tasks. The new models are evaluated on a Web document ranking task using a real-world data set. Results show that our best model significantly outperforms other latent semantic models, which were considered state-of-the-art in the performance prior to the work presented in this paper.
BSML: bio-synergy modeling language for multi-component and multi-target analysis
Authors: Woochang Hwang, Jaejoon Choi, Jinmyung Jung, Doheon Lee
Venue: CIKM
Year: 2013

Multi-compound drugs are considered as the most promising solution to overcome the limited efficacy and off-target effect of drugs. However, identifying promising multiple compounds by experimental tests requires overwhelming costs and a number of tests. Systems biology-based approaches are regarded as one of the most promising strategy. To predict responses of drugs in biological systems is one of aims of Systems biology.
Exploiting trustors as well as trustees in trust-based recommendation
Authors: Won-Seok Hwang, Shaoyu Li, Sang-Wook Kim, Ho Jin Choi
Venue: CIKM
Year: 2013

In a trust network, two users who are connected by a trust relationship tend to have similar interests. Based on this observation, existing trust-aware recommendation methods predict ratings for a target user on unseen items by referencing to ratings of those users who are reachable from the target user in the forward direction of trustor-trustee relationship through the trust network. However, these methods have overlooked the possibility of utilizing the ratings of those users reachable in the backward direction, which may also have similar interests. In this paper, we investigate this possibility by identifying and adding these users to the existing methods when predicting ratings for the target user. We perform a series of experiments and observe that our approach improves the coverage while preserving the accuracy.