OpenRaiser/Intern-Atlas huggingface dataset:
Using Local Information to Significantly Improve Classification Performance
Authors: Wei Liu, Dong Lee, Rao Kotagiri
Venue: CIKM
Year: 2014

In this research we propose to derive new features based on data samples' local information with the aim of improving the performance of general supervised learning algorithms. The creation of new features is inspired by the measure of average precision which is known to be a robust measure that is insensitive to the number of retrieved items in information retrieval. We use the idea of average precision to weight the neighbours of an instance and show that this weighting strategy is insensitive to the number of neighbours in the locality. Information captured in the new features allows a general classifier to learn additional useful peripheral knowledge that are helpful in building effective classification models. We comprehensively evaluate our method on real datasets and the results show substantial improvements in the performance of classifiers including SVM, Bayesian networks, random forest, and C4.5.
AMiner-mini: A People Search Engine for University
Authors: Jingyuan Liu, Debing Liu, Xingyu Yan, Li Dong, Ting Zeng, Yutao Zhang, Jie Tang
Venue: CIKM
Year: 2014

We present a distributed academic search and mining system? AMiner-mini. The system offers intra- and inter- university level academic search and mining services. It integrates academic data from multiple sources and performs disambiguation for people names, which is a fundamental issue for searching people. We employ a two-phases approach that formalizes the disambiguation problem into a HMRF framework, which significantly improves the disambiguation performance. Based on the disambiguation results, AMiner-mini offers a people search function, which returns experts (or related researchers) for a given query by the user. The user can also choose different metrics to rank the search results and explore the results from different dimensions. The system is designed in a distributed structure. It can be deployed in a university as a stand-alone system for finding the right people who are working on a research topic. Multiple distributed systems can be also connected via Web services and perform search or mining in an asynchronous way and return the combination results. We have deployed the system in Tsinghua University and feedback from university academic users shows that the system worked well and achieved its primary objective.
Distributed Graph Summarization
Authors: Xingjie Liu, Yuanyuan Tian, Qi He, Wang-Chien Lee, John McPherson
Venue: CIKM
Year: 2014

Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods.
Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining
Authors: Yang Liu, Songhua Xu, Lian Duan
Venue: CIKM
Year: 2014

With the rapid development of Web 2.0 and the Internet of things, predicting relationships in heterogeneous networks has evolved as a heated research topic. Traditionally, people analyze existing relationships in heterogeneous networks that relate in a particular way to a target relationship of interest to predict the emergence of the target relationship. However most existing methods are incapable of systematically identifying relevant relationships useful for the prediction task, especially those relationships involving multiple objects of heterogeneous types, which may not rest on a simple path in the concerned heterogeneous network. Another problem with the current practice is that the existing solutions often ignore the dynamic evolution of the network structure after the introduction of newly emerged relationships. To overcome the first limitation, we propose a new algorithm that can systematically and comprehensively detect relevant relationships useful for the prediction of an arbitrarily given target relationship through a disciplined graph searching process. To address the second limitation, the new algorithm leverages a series of temporally-sensitive features for the relationship occurrence prediction via a supervised learning approach. To explore the effectiveness of the new algorithm, we apply the prototype implementation of the algorithm on the DBLP bibliographic network to predict the author citation relationships and compare the algorithm performance with that of a state-of-the-art peer method and a series of baseline methods. The comparison shows consistently higher prediction accuracy under a range of prediction scenarios.
Identifying Your Customers in Social Networks
Authors: Chun-Ta Lu, Hong-Han Shuai, Philip S. Yu
Venue: CIKM
Year: 2014

Personal social networks are considered as one of the most influential sources in shaping a customer's attitudes and behaviors. However, the interactions with friends or colleagues in social networks of individual customers are barely observable in most e-commerce companies. In this paper, we study the problem of customer identification in social networks, i.e., connecting customer accounts at e-commerce sites to the corresponding user accounts in online social networks such as Twitter. Identifying customers in social networks is a crucial prerequisite for many potential marketing applications. These applications, for example, include personalized product recommendation based on social correlations, discovering community of customers, and maximizing product adoption and profits over social networks.
EgoCentric: Ego Networks for Knowledge-based Short Text Classification
Authors: William Lucia, Elena Ferrari
Venue: CIKM
Year: 2014

Classification of short text messages is becoming more and more relevant in these years, where billion of users use online social networks to communicate with other people. Understanding message content can have a huge impact on many data analysis processes, ranging from the study of online social behavior to targeted advertisement, to security and privacy purposes. In this paper, we propose a new unsupervised knowledge-based classifier for short text messages, where each category is represented by an ego-network. A short text is classified into a category depending on how far its words are from the ego of that category. We show how this technique can be used both in single label and in multi-label classification, and how it outperforms the state of the art for short text messages classification.
Improving Recommendation Accuracy by Combining Trust Communities and Collaborative Filtering
Authors: Xiao Ma, Hongwei Lu, Zaobin Gan
Venue: CIKM
Year: 2014

With the booming of online social networks, social trust has been used to cluster users in recommender systems. It has been proven to improve the recommendation accuracy when trust communities are integrated into memory-based collaborative filtering algorithms. However, existing trust community mining methods only consider the trust relationships, regardless of the distrust information. In this paper, considering both the trust and distrust relationships, a SVD signs based community mining method is proposed to process the trust relationship matrix in order to discover the trust communities. A modified trust metric which considers a given user's expertise level in a community is presented to obtain the indirect trust values between users. Then some missing ratings of the given user are complemented by the weighted average preference of his/her trusted neighbors selected in the same community during the random walk procedures. Finally, the prediction for a given item is generated by the conventional collaborative filtering. The comparison experiments on Epinions data set demonstrate that our approach outperforms other state-of-the-art methods in terms of RMSE and RC.
Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation
Authors: Parvaz Mahdabi, Fabio Crestani
Venue: CIKM
Year: 2014

Prior art search or recommending citations for a patent application is a challenging task. Many approaches have been proposed and shown to be useful for prior art search. However, most of these methods do not consider the network structure for integrating and diffusion of different kinds of information present among tied patents in the citation network. In this paper, we propose a method based on a time-aware random walk on a weighted network of patent citations, the weights of which are characterized by contextual similarity relations between two nodes on the network. The goal of the random walker is to find influential documents in the citation network of a query patent, which can serve as candidates for drawing query terms and bigrams for query refinement. The experimental results on CLEF-IP datasets (CLEF-IP 2010 and CLEF-IP 2011) show the effectiveness of encoding contextual similarities (common classification codes, common inventor, and common applicant) between nodes in the citation network. Our proposed approach can achieve significantly better results in terms of recall and Mean Average Precision rates compared to strong baselines of prior art search.
DUBMOD14 - International Workshop on Data-driven User Behavioral Modeling and Mining from Social Media
Authors: Jalal Mahmud, Jeffrey Nichols, Michelle X. Zhou, James Caverlee, Yi Zeng, Liang Chen, John O&apos, Donovan
Venue: CIKM
Year: 2014

Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. These data can be used to better understand people (e.g., personality traits, perceptions, and preferences) and predict their behavior. As a result, a deeper understanding of users and their behavior can benefit a wide range of intelligent applications, such as advertising, social recommender systems, and personalized knowledge management. These applications will also benefit individual users themselves and optimize their experience across a wide variety of domains, such as retail, healthcare, and education. Since mining and understanding user behavior from social media often requires interdisciplinary effort, including machine learning, text mining, human-computer interaction, and social science, our workshop aims to bring together researchers and practitioners from multiple fields to discuss the creation of deeper models of individual users by mining the content that they publish and the social networking behavior that they exhibit.
Leverage the Associations between Documents, Subject Headings and Terms to Enhance Retrieval
Authors: Jin Mao, Kun Lu
Venue: CIKM
Year: 2014

Literatures in medical domain are often annotated with subject headings by professionals to help information seeking via manifesting the subjects of documents, where subject headings serve as the pivot language between documents and users. Current information retrieval methods using subject headings have not fully exploited the potential of subject headings yet. Both positive and negative results have been reported. In this paper, we explored the three-layer structure of documents annotated with subject headings, including document layer, concept layer (i.e. subject headings) and term layer, and then we proposed a concept-enhanced relevance model. The document-concept associations are mined to generate conceptual representations for documents and the concept-term associations are quantified and used to represent concepts as language models. By embedding these associations, subject headings are applied to enrich the document models in the estimation process of relevance model. The experiments carried out on two medical collections showed the improvements of our model by comparing with three state-of-the-art baselines. Therefore, if exploited appropriately, such manually curated annotations as subject headings can become an effective tool to enhance information retrieval.
Nonlinear Classification via Linear SVMs and Multi-Task Learning
Authors: Xue Mao, Ou Wu, Weiming Hu, Peter O&apos, Donovan
Venue: CIKM
Year: 2014

Kernel SVM is prohibitively expensive when dealing with large nonlinear data. While ensembles of linear classifiers have been proposed to address this inefficiency, these methods are time-consuming or lack robustness. We propose an efficient classifier for nonlinear data using a new iterative learning algorithm, which partitions the data into clusters, and then trains a linear SVM for each cluster. These two steps are combined into a graphical model, with the parameters estimated efficiently using the EM algorithm. During training, clustered multi-task learning is used to capture the relatedness among the multiple linear SVMs and avoid overfitting. Experimental results on benchmark datasets show that our method outperforms state-of-the-art methods. During prediction, it also obtains comparable classification performance to kernel SVM, with much higher efficiency.
Knowledge Management for Keyword Search over Data Graphs
Authors: Yosi Mass, Yehoshua Sagiv
Venue: CIKM
Year: 2014

This demo presents exploratory keyword search over data graphs by means of semantic facets. The demo starts with a keyword search over data graphs. Answers are first ranked by an existing search engine that considers their textual relevance and semantic structure. The user can then explore the answers through facets of structural patterns (i.e., schemas) as well as through other features. A particular way of presenting answers in a compact form is also supported and is applicable when looking for a single entity that connects the keywords. The demo is based on a working prototype that users can try on their own. It includes five data graphs that are quite diversified. In particular, three of them were generated from relational databases and two - from RDF triples. The demo shows that the system enables users to easily and quickly perform various search tasks by means of exploration, filtering and summarization.
Incremental Update Summarization: Adaptive Sentence Selection based on Prevalence and Novelty
Authors: Richard McCreadie, Craig Macdonald, Iadh Ounis
Venue: CIKM
Year: 2014

The automatic summarization of long-running events from news steams is a challenging problem. A long-running event can contain hundreds of unique 'nuggets' of information to summarize, spread-out over its lifetime. Meanwhile, information reported about it can rapidly become outdated and is often highly redundant. Incremental update summarization (IUS) aims to select sentences from news streams to issue as updates to the user, summarising that event over time. The updates issued should cover all of the key nuggets concisely and before the information contained in those nuggets becomes outdated. Prior summarization approaches when applied to IUS can fail, since they define a fixed summary length that cannot effectively account for the different magnitudes and varying rate of development of such events. In this paper, we propose a novel IUS approach that adaptively alters the volume of content issued as updates over time with respect to the prevalence and novelty of discussions about the event. It incorporates existing state-of-the-art summarization techniques to rank candidate sentences, followed by a supervised regression model that balances novelty, nugget coverage and timeliness when selecting sentences from the top ranks. We empirically evaluate our approach using the TREC 2013 Temporal Summarization dataset extended with additional assessments. Our results show that by adaptively adjusting the number of sentences to select over time, our approach can nearly double the performance of effective summarization baselines.
"Picture the scene...";: Visually Summarising Social Media Events
Authors: Philip J. McParlane, Andrew James McMinn, Joemon M. Jose
Venue: CIKM
Year: 2014

Due to the advent of social media and web 2.0, we are faced with a deluge of information; recently, research efforts have focused on filtering out noisy, irrelevant information items from social media streams and in particular have attempted to automatically identify and summarise events. However, due to the heterogeneous nature of such social media streams, these efforts have not reached fruition. In this paper, we investigate how images can be used as a source for summarising events. Existing approaches have considered only textual summaries which are often poorly written, in a different language and slow to digest. Alternatively, images are "worth 1,000 words" and are able to quickly and easily convey an idea or scene. Since images in social media can also be noisy, irrelevant and repetitive, we propose new techniques for their automatic selection, ranking and presentation. We evaluate our approach on a recently created social media event data set containing 365k tweets and 50 events, for which we extend by collecting 625k related images. By conducting two crowdsourced evaluations, we firstly show how our approach overcomes the problems of automatically collecting relevant and diverse images from noisy microblog data, before highlighting the advantages of multimedia summarisation over text based approaches.
PIKM 2014: The 7th ACM Workshop for Ph.D. Students in Information and Knowledge Management
Authors: Gerard de Melo, Mouna Kacimi, Aparna S. Varde
Venue: CIKM
Year: 2014

PIKM workshop offers to Ph.D. students the possibility to bring their work to an international and interdisciplinary research community, and create a network of young researchers to exchange and develop new and promising ideas. Similarly to the CIKM, PIKM workshop covers a wide range of topics in the areas of databases, information retrieval and knowledge management.
Semantic Approximate Keyword Query Based on Keyword and Query Coupling Relationship Analysis
Authors: Xiangfu Meng, Longbing Cao, Jingyu Shao
Venue: CIKM
Year: 2014

Due to imprecise query intention, Web database users often use a limited number of keywords that are not directly related to their precise query to search information. Semantic approximate keyword query is challenging but helpful for specifying such query intent and providing more relevant answers. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query approach which generates semantic approximate answers by identifying a set of keyword queries from the query history whose semantics are related to the given keyword query. To capture the semantic relationships between keywords, a semantic coupling relationship analysis model is introduced to model both the intra- and inter-keyword couplings. Building on the coupling relationships between keywords, the semantic similarity of different keyword queries is then measured by a semantic matrix. The representative queries in query history are identified and then a priori order of remaining queries corresponding to each representative query in an off-line preprocessing step is created. These representative queries and associated orders are then used to expeditiously generate top-k ranked semantically related keyword queries. We demonstrate that our coupling relationship analysis model can accurately capture the semantic relationships both between keywords and queries. The efficiency of top-k keyword query selection algorithm is also demonstrated.
Semantic Search at Yahoo!
Authors: Peter Mika
Venue: CIKM
Year: 2014

Semantic search refers to a broad array of methods that aim to improve retrieval by interpreting queries beyond the traditional weighted bag of words model of document retrieval. In this talk, we will focus on the subset of these methods that rely on explicit semantic annotations, i.e. linking queries and text to items in a Knowledge Base. We will discuss techniques of entity linking on queries and documents, and the potential impact of these methods on improving performance on the classical ad-hoc document retrieval task. We will also discuss some novel tasks, including entity retrieval and related entity recommendations, and their implementation in Yahoo Search. We will close by considering some of the challenges that are specific to developing search services in a mobile context.
A Dynamic Reconstruction Approach to Topic Summarization of User-Generated-Content
Authors: Zhaoyan Ming, Jintao Ye, Tat-Seng Chua
Venue: CIKM
Year: 2014

User generated contents (UGCs) from various social media sites give analysts the opportunity to obtain a comprehensive and dynamic view of any topic from multiple heterogeneous information sources. Summarization provides a promising means of distilling the overview of the targeted topic by aggregating and condensing the related UGCs. However, the mass volume, uneven quality, and dynamics of UGCs, pose new challenges that are not addressed by existing multi-document summarization techniques. In this paper, we introduce a timely task of dynamic structural and textual summarization. We generate topic hierarchy from the UGCs as a high level overview and structural guide for exploring and organizing the content. To capture the evolution of events in the content, we propose a unified dynamic reconstruction approach to detect the update points and generate the time-sequence textual summary. To enhance the expressiveness of the reconstruction space, we further use the topic hierarchy to organize the UGCs and the hierarchical subtopics to augment the sentence representation. Experimental comparison with the state-of-the-art summarization models on a multi-source UGC dataset shows the superiority of our proposed methods. Moreover, we conducted a user study on our usability enhancement measures. It suggests that by disclosing some meta information of the summary generation process in the proposed framework, the time-sequence textual summaries can pair with the structural overview of the topic hierarchy to achieve interpretable and verifiable summarization.
Cross-Device Search
Authors: George D. Montañez, Ryen W. White, Xiao Huang
Venue: CIKM
Year: 2014

Ownership and use of multiple devices such as desktop computers, smartphones, and tablets is increasing rapidly. Search is popular and people often perform search tasks that span device boundaries. Understanding how these devices are used and how people transition between them during information seeking is essential in developing search support for a multi-device world. In this paper, we study search across devices and propose models to predict aspects of cross-device search transitions. We characterize multi-device search across four device types, including aspects of search behavior on each device (e.g., topics of interest) and characteristics of device transitions. Building on the characterization, we learn models to predict various aspects of cross-device search, including the next device used for search. This enables many applications. For example, accurately forecasting the device used for the next query lets search engines proactively retrieve device-appropriate content (e.g., short documents for smartphones), while knowledge of the current device combined with device-specific topical interest models may assist in better query-sense disambiguation. %to help the searcher once they transition to the target device.