OpenRaiser/Intern-Atlas huggingface dataset:
Construction of tag ontological graphs by locally minimizing weighted average hops
Authors: Chetan Kumar Verma, Vijay Mahadevan, Nikhil Rasiwasia, Gaurav Aggarwal, Ravi Kant, Alejandro Jaimes, Sujit Dey
Venue: WWW
Year: 2014

We present a data-driven approach for the construction of ontological graphs on a set of image tags obtained from annotated image corpus. We treat each tag as a node in a graph, and starting with a preliminary graph obtained using WordNet, we propose the graph construction as a refinement of the preliminary graph using corpus statistics. Towards this, we formulate an optimization problem which is solved using a local search based approach. To evaluate the constructed ontological graphs, we propose a novel task which involves associating test images with tags while observing partial set of associated tags.
Aligning web collaboration tools with research data for scholars
Authors: Laurens De Vocht, Selver Softic, Erik Mannens, Martin Ebner, Rik Van de Walle
Venue: WWW
Year: 2014

Resources for research are not always easy to explore, and rarely come with strong support for identifying, linking and selecting those that can be of interest to scholars. In this work we introduce a model that uses state-of-the-art semantic technologies to interlink structured research data and data from Web collaboration tools, social media and Linked Open Data. We use this model to build a platform that connects scholars, using their profiles as a starting point to explore novel and relevant content for their research. Scholars can easily adapt to evolving trends by synchronizing new social media accounts or collaboration tools and integrate then with new datasets. We evaluate our approach by a scenario of personalized exploration of research repositories where we analyze real world scholar profiles and compare them to a reference profile.
Spatial and temporal patterns of online food preferences
Authors: Claudia Wagner, Philipp Singer, Markus Strohmaier
Venue: WWW
Year: 2014

Since food is one of the central elements of all human beings, a high interest exists in exploring temporal and spatial food and dietary patterns of humans. Predominantly, data for such investigations stem from consumer panels which continuously capture food consumption patterns from individuals and households. In this work we leverage data from a large online recipe platform which is frequently used in the German speaking regions in Europe and explore (i) the association between geographic proximity and shared food preferences and (ii) to what extent temporal information helps to predict the food preferences of users. Our results reveal that online food preferences of geographically closer regions are more similar than those of distant ones and show that specific types of ingredients are more popular on specific days of the week. The observed patterns can successfully be mapped to known real-world patterns which suggests that existing methods for the investigation of dietary and food patterns (e.g., consumer panels) may benefit from incorporating the vast amount of data generated by users browsing recipes on the Web.
Semantic stability in social tagging streams
Authors: Claudia Wagner, Philipp Singer, Markus Strohmaier, Bernardo A. Huberman
Venue: WWW
Year: 2014

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language phenomena alone.
A behavior observation tool (BOT) for mobile device network connection logs
Authors: Ting Wang
Venue: WWW
Year: 2014

With the advances of sensory, satellite and mobile communication technologies in recent decades, locational data become widely available. A lot of work has been developed to find useful information from these data, and various approaches have been proposed. In this work, we aim to use one specific type of locational data -- network connection logs of mobile devices, which is widely available and easily accessible to telecom companies, to identify and extract active areas of users. This is a challenging topic due to the existence of inaccurate location and fluctuating log time intervals of this kind of data. In order to observe user behavior from this kind of data set, we propose a new algorithm, namely Behavior Observation Tool (BOT), which uses Convex Hull Algorithm with sliding time windows to model the user's movement, and thus knowledge about the user's lifestyle and habits can extracted from the mobile device network logs.
A hierarchical Dirichlet model for taxonomy expansion for search engines
Authors: Jingjing Wang, Changsung Kang, Yi Chang, Jiawei Han
Venue: WWW
Year: 2014

Emerging trends and products pose a challenge to modern search engines since they must adapt to the constantly changing needs and interests of users. For example, vertical search engines, such as Amazon, eBay, Walmart, Yelp and Yahoo! Local, provide business category hierarchies for people to navigate through millions of business listings. The category information also provides important ranking features that can be used to improve search experience. However, category hierarchies are often manually crafted by some human experts and they are far from complete. Manually constructed category hierarchies cannot handle the ever-changing and sometimes long-tail user information needs. In this paper, we study the problem of how to expand an existing category hierarchy for a search/navigation system to accommodate the information needs of users more comprehensively. We propose a general framework for this task, which has three steps: 1) detecting meaningful missing categories; 2) modeling the category hierarchy using a hierarchical Dirichlet model and predicting the optimal tree structure according to the model; 3) reorganizing the corpus using the complete category structure, i.e., associating each webpage with the relevant categories from the complete category hierarchy. Experimental results demonstrate that our proposed framework generates a high-quality category hierarchy and significantly boosts the retrieval performance.
Similarity-based web browser optimization
Authors: Haoyu Wang, Mengxin Liu, Yao Guo, Xiangqun Chen
Venue: WWW
Year: 2014

The performance of web browsers has become a major bottleneck when dealing with complex webpages. Many calculation redundancies exist when processing similar webpages, thus it is possible to cache and reuse previously calculated intermediate results to improve web browser performance significantly. In this paper, we propose a similarity-based optimization approach to improve webpage processing performance of web browsers. Through caching and reusing of style properties calculated previously, we are able to eliminate the redundancies caused by processing similar webpages from the same website. We propose a tree-structured architecture to store style properties to facilitate efficient caching and reuse. Experiments on webpages of various websites show that the proposed technique can speed up the webpage loading process by up to 68% and reduce the redundant style calculations by up to 77% for the first visit to a webpage with almost negligible overhead.
Answering provenance-aware regular path queries on RDF graphs using an automata-based algorithm
Authors: Xin Wang, Jun Ling, Junhu Wang, Kewen Wang, Zhiyong Feng
Venue: WWW
Year: 2014

This paper presents an automata-based algorithm for answering the \emph{provenance-aware} regular path queries (RPQs) over RDF graphs on the Semantic Web. The provenance-aware RPQs can explain why pairs of nodes in the classical semantics appear in the result of an RPQ. We implement a parallel version of the automata-based algorithm using the Pregel framework Giraph to efficiently evaluate provenance-aware RPQs on large RDF graphs. The experimental results show that our algorithms are effective and efficient to answer provenance-aware RPQs on large real-world RDF graphs.
Observing the web by understanding the past: archival internet research
Authors: Matthew S. Weber
Venue: WWW
Year: 2014

This paper discusses the challenges and opportunities for using archival Internet data in order to observe a host of social science phenomena. Specifically, this paper introduces HistoryTracker, a new tool for accessing and extracting archived data from the Internet Archive, the largest repository of archived Web data in existence. The HistoryTracker tool serves to create a Web observatory that allows scholars to study the history of the Web. HistoryTracker takes advantages of Hadoop processing capacity, and allows researchers to extract large swaths of archived data into a link list format that can be easily transferred to a number of other analytical tools. A brief illustration of the use of HistoryTracker is presented demonstrating the use of the tool. Finally, a number of continuing research challenges are discussed, and future research opportunities are outlined.
Knowledge base completion via search-based question answering
Authors: Robert West, Evgeniy Gabrilovich, Kevin Murphy, Shaohua Sun, Rahul Gupta, Dekang Lin
Venue: WWW
Year: 2014

Over the past few years, massive amounts of world knowledge have been accumulated in publicly available knowledge bases, such as Freebase, NELL, and YAGO. Yet despite their seemingly huge size, these knowledge bases are greatly incomplete. For example, over 70% of people included in Freebase have no known place of birth, and 99% have no known ethnicity. In this paper, we propose a way to leverage existing Web-search-based question-answering technology to fill in the gaps in knowledge bases in a targeted way. In particular, for each entity attribute, we learn the best set of queries to ask, such that the answer snippets returned by the search engine are most likely to contain the correct value for that attribute. For example, if we want to find Frank Zappa's mother, we could ask the query `who is the mother of Frank Zappa'. However, this is likely to return `The Mothers of Invention', which was the name of his band. Our system learns that it should (in this case) add disambiguating terms, such as Zappa's place of birth, in order to make it more likely that the search results contain snippets mentioning his mother. Our system also learns how many different queries to ask for each attribute, since in some cases, asking too many can hurt accuracy (by introducing false positives). We discuss how to aggregate candidate answers across multiple queries, ultimately returning probabilistic predictions for possible values for each attribute. Finally, we evaluate our system and show that it is able to extract a large number of facts with high confidence.
Wikipedia as a time machine
Authors: Stewart Whiting, Joemon M. Jose, Omar Alonso
Venue: WWW
Year: 2014

Wikipedia encyclopaedia projects, which consist of vast collections of user-edited articles covering a wide range of topics, are among some of the most popular websites on internet. With so many users working collaboratively, mainstream events are often very quickly reflected by both authors editing content and users reading articles. With temporal signals such as changing article content, page viewing activity and the link graph readily available, Wikipedia has gained attention in recent years as a source of temporal event information. This paper serves as an overview of the characteristics and past work which support Wikipedia (English, in this case) for time-aware information retrieval research. Furthermore, we discuss the main content and meta-data temporal signals available along with illustrative analysis. We briefly discuss the source and nature of each signal, and any issues that may complicate extraction and use. To encourage further temporal research based on Wikipedia, we have released all the distilled datasets referred to in this paper.
3DOC: 3D object CAPTCHA
Authors: Simon S. Woo, Beomjun Kim, Woochan Jun, Jingul Kim
Venue: WWW
Year: 2014

Current 2D CAPTCHA mechanisms can be easily defeated by character recognition and segmentation attacks by automated machines. Recently, 3D CAPTCHA schemes have been proposed to overcome the weaknesses of 2D CAPTCHA for a few websites. However, researchers also demonstrate the offline pre-processing techniques to break 3D CAPTCHA. In this work, we propose a novel 3D object based CAPTCHA scheme that projects the CAPTCHA image over a 3D object. We develop the prototype and present the proof-of-concept of 3D object based CAPTCHA scheme to protect websites against automated attacks.
Detecting in-situ identity fraud on social network services: a case study on facebook
Authors: Shan-Hung Wu, Man-Ju Chou, Chun-Hsiung Tseng, Yuh-Jye Lee, Kuan-Ta Chen
Venue: WWW
Year: 2014

In this paper, we propose to use a continuous authentication approach to detect the in-situ identity fraud incidents, which occur when the attackers use the same devices and IP addresses as the victims. Using Facebook as a case study, we show that it is possible to detect such incidents by analyzing SNS users' browsing behavior. Our experiment results demonstrate that the approach can achieve reasonable accuracy given a few minutes of observation time.
Improving query suggestion through noise filtering and query length prediction
Authors: Liang Wu, Bin Cao, Yuanchun Zhou, Jianhui Li
Venue: WWW
Year: 2014

Clustering-based methods are commonly used in Web search engines for query suggestion. Clustering is useful in reducing the sparseness of data. However, it also introduces noises and ignores the sequential information of query refinements in search sessions. In this paper, we propose to improve cluster based query suggestion from two perspectives: filtering out unrelated query candidates and predicting the refinement direction. We observe two major refinements behaviors. One is to simplify the original query and the other is to specify it. Both could be modeled by predicting the length (number of terms) of queries when candidates are being ranked. Two experimental results on the real query logs of a commercial search engine demonstrate the effectiveness of the proposed approaches.
Folksonomy based socially-aware recommendation of scholarly papers for conference participants
Authors: Feng Xia, Nana Yaw Asabere, Haifeng Liu, Nakema Deonauth, Fengqi Li
Venue: WWW
Year: 2014

Due to the significant proliferation of scholarly papers in both conferences and journals, recommending relevant papers to researchers for academic learning has become a substantial problem. Conferences, in comparison to journals have an aspect of social learning, which allows personal familiarization through various interactions among researchers. In this paper, we improve the social awareness of participants of smart conferences by proposing an innovative folksonomy-based paper recommendation algorithm, namely, Socially-Aware Recommendation of Scholarly Papers (SARSP). Our proposed algorithm recommends scholarly papers, issued by Active Participants (APs), to other Group Profile participants at the same smart conference based on similarity of their research interests. Furthermore, through computation of social ties, SARSP generates effective recommendations of scholarly papers to participants who have strong social ties with an AP. Through a relevant real-world dataset, we evaluate our proposed algorithm. Our experimental results verify that SARSP has encouraging improvements over other existing methods.
Multi-category item recommendation using neighborhood associations in trust networks
Authors: Feng Xia, Haifeng Liu, Nana Yaw Asabere, Wei Wang, Zhuo Yang
Venue: WWW
Year: 2014

This paper proposes a novel recommendation method called RecDI. In the multi-category item recommendation domain, RecDI is designed to combine user ratings with information involving user's direct and indirect neighborhood associations. Through relevant benchmarking experiments on two real-world datasets, we show that RecDI achieves better performance than other traditional recommendation methods, which demonstrates the effectiveness of RecDI.
Fine-grained data partitioning framework for distributed database systems
Authors: Ning Xu
Venue: WWW
Year: 2014

With the increasing size of web data and widely adopted parallel cloud computing paradigm, distributed database and other distributed data processing systems, for example Pregel and GraphLab, use data partitioning technology to divide the large data set. By default, these systems use hash partitioning to randomly assign data to partitions, which leads to huge network traffic between partitions. Fine-grained partitioning can better allocate data and minimize the number of nodes involved within a transaction or job while balancing the workload across data nodes as well. In this paper, we propose a novel prototype system, Lute, to provide highly efficient fine-grained partitioning scheme for these distributed systems. Lute maintains a lookup table for each partitioned data set that maps a key to a set of partition ID(s). We use a novel lookup table technology that provides low cost of reading and writing lookup table. Lute provides transaction support and high concurrency writing with Multi Version Concurrency Control (MVCC) as well.
Tag propagation based recommendation across diverse social media
Authors: Deqing Yang, Yanghua Xiao, Yangqiu Song, Junjun Zhang, Kezun Zhang, Wei Wang
Venue: WWW
Year: 2014

Many real applications demand accurate cross-domain recommendation, e.g., recommending a Weibo (the largest Chinese Twitter) user with the products in an e-commerce Web site. Since many social media have rich tags on both items or users, tag-based profiling became popular for recommendation. However, most previous recommendation approaches have low effectiveness in handling sparse data or matching tags from different social media. Addressing these problems, we first propose an optimized local tag propagation algorithm to generate tags for profiling Weibo users and then use a Chinese knowledge graph accompanied by an improved ESA (explicit semantic analysis) for semantic matching of cross-domain tags. Empirical comparisons to the state-of-the-art approaches justify the efficiency and effectiveness of our approaches.
SoRank: incorporating social information into learning to rank models for recommendation
Authors: Weilong Yao, Jing He, Guangyan Huang, Yanchun Zhang
Venue: WWW
Year: 2014

Most existing learning to rank based recommendation methods only use user-item preferences to rank items, while neglecting social relations among users. In this paper, we propose a novel, effective and efficient model, SoRank, by integrating social information among users into listwise ranking model to improve quality of ranked list of items. In addition, with linear complexity to the number of observed ratings, SoRank is able to scale to very large dataset. Experimental results on publicly available dataset demonstrate the effectiveness of SoRank.
Me-link: link me to the media - fusing audio and visual cues for robust and efficient mobile media interaction
Authors: Chun-Yen Yeh, Yu-Ming Hsu, Hsinfu Huang, Hong-Wun Jheng, Yu-Chuan Su, Tzu-Hsuan Chiu, Winston H. Hsu
Venue: WWW
Year: 2014

In this demo, we present a scalable mobile video recognition system, named "Me-link," based on progressive fusion of light-weight audio visual features. With our system, users only have to point the mobile camera to the video they are interested in. The system will capture the frames and sounds, then retrieve relevant information immediately. As the users hold the mobile longer, the system progressively aggregates the cues temporally and then returns more accurate results. We also consider the real world noisy environment, where users may not get clear visual or audio signals. In the aggregation step of audio and visual cues, our system automatically detects the available channel for the final rank. On the server side, users can upload the videos with information via website. Besides, we also link the streaming signals so that users can get the real time broadcasting with ``Me-link".
Exploring social activeness and dynamic interest in community-based recommender system
Authors: Bin Yin, Yujiu Yang, Wenhuang Liu
Venue: WWW
Year: 2014

Community-based recommender systems have attracted much research attention. Forming communities allows us to reduce data sparsity and focus on discovering the latent characteristics of communities instead of individuals. Previous work focused on how to detect the community using various algorithms. However, they failed to consider users' social attributes, such as social activeness and dynamic interest, which have strong correlations to users' preference and choice. Intuitively, people have different social activeness in a social network. Ratings from users with high activeness are more likely to be trustworthy. Temporal dynamic of interest is also significant to user's preference. In this paper, we propose a novel community-based framework. We first employ PLSA-based model incorporating social activeness and dynamic interest to discover communities. Then the state-of-the-art matrix factorization method is applied on each of the communities. The experiment results on two real world datasets validate the effectiveness of our method for improving recommendation performance.
PRISM: a system for weighted multi-color browsing of fashion products
Authors: Donggeun Yoo, Kyunghyun Paeng, Sunggyun Park, Jungin Lee, Seungwook Paek, Sung-Eui Yoon, In-So Kweon
Venue: WWW
Year: 2014

Multiple color search technology helps users find fashion products in a more intuitive manner. Although fashion product images can be represented not only by a set of dominant colors but also by the relative ratio of colors, current online fashion shopping malls often provide rather simple color filters. In this demo, we present PRISM (Perceptual Representation of Image SiMilarity), a weighted multi-color browsing system for fashion products retrieval. Our system combines widely accepted backend web service stacks and various computer vision techniques including a product area parsing and a compact yet effective multi-color description. Finally, we demonstrate the benefits of PRISM system via web service in which users freely browse fashion products.