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• 79 type InProceedings.
• 79 label "Constructing Folksonomies from User-Specified Relations on Flickr".
• 79 sameAs 79.
• 79 abstract "Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use userspecified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.".
• 79 hasAuthorList authorList.
• 79 isPartOf proceedings.
• 79 keyword "Social Networks and Web 2.0".
• 79 title "Constructing Folksonomies from User-Specified Relations on Flickr".
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• item-1 hasContent anon-plangprasopchok.
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• item-2 hasContent kristina-lerman.
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• 8 type InProceedings.
• 8 label "Enhancing Diversity, Coverage and Balance for Summarization through Structure Learning".
• 8 sameAs 8.
• 8 abstract "Document summarization plays an increasingly important role with the exponential growth of documents on the Web. Many supervised and unsupervised approaches have been proposed to generate summaries from documents. However, these approaches seldom simultaneously consider summary diversity, coverage, and balance issues which to a large extent determine the quality of summaries. In this paper, we consider extract-based summarization emphasizing the following three requirements: 1) diversity in summarization, which seeks to reduce redundancy among sentences in the summary; 2) sufficient coverage, which focuses on avoiding the loss of the document's main information when generating the summary; and 3) balance, which demands that different aspects of the document need to have about the same relative importance in the summary. We formulate the extract-based summarization problem as learning a mapping from a set of sentences of a given document to a subset of the sentences that satisfies the above three requirements. The mapping is learned by incorporating several constraints in a structure learning framework, and we explore the graph structure of the output variables and employ structural SVM for solving the resulted optimization problem. Experiments on the DUC2001 data sets demonstrate significant performance improvements in terms of F1 and ROUGE metrics.".
• 8 hasAuthorList authorList.
• 8 isPartOf proceedings.
• 8 keyword "Data Mining".
• 8 title "Enhancing Diversity, Coverage and Balance for Summarization through Structure Learning".
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• item-1 hasContent liangda-li.
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• item-2 hasContent ke-zhou.
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• item-3 hasContent gui-rong-xue.
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• item-4 hasContent hongyuan-zha.
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• item-5 hasContent yong-yu.
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• 80 type InProceedings.
• 80 label "Mining Interesting Locations and Travel Sequences from GPS Trajectories".
• 80 sameAs 80.
• 80 abstract "The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.".
• 80 hasAuthorList authorList.
• 80 isPartOf proceedings.
• 80 keyword "User Interfaces and Mobile Web".
• 80 title "Mining Interesting Locations and Travel Sequences from GPS Trajectories".
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• item-1 hasContent yu-zheng.
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• item-2 hasContent lizhu-zhang.
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• item-3 type ListItem.
• item-3 hasContent xing-xie.
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• item-4 type ListItem.
• item-4 hasContent wei-ying-ma.
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• 81 type InProceedings.
• 81 label "Computers and iPhones and Mobile Phones, oh my!".
• 81 sameAs 81.
• 81 abstract "We present a logs-based comparison of search patterns across three platforms: computers, iPhones and conventional mobile phones. Our goal is to understand how mobile search users differ from computer-based search users, and we focus heavily on the distribution and variability of tasks that users perform from each platform. The results suggest that search usage is much more focused for the average mobile user than for the average computer-based user. However, search behavior on high-end phones resembles computer-based search behavior more so than mobile search behavior. A wide variety of implications follow from these findings. First, there is no single search interface which is suitable for all mobile phones. We suggest that for the higher-end phones, a close integration with the standard computer-based interface (in terms of personalization and available feature set) would be beneficial for the user, since these phones seem to be treated as an extension of the users' computer. For all other phones, there is a huge opportunity for personalizing the search experience for the user's "mobile needs", as these users are likely to repeatedly search for a single type of information need on their phone.".
• 81 hasAuthorList authorList.
• 81 isPartOf proceedings.
• 81 keyword "User Interfaces and Mobile Web".
• 81 title "Computers and iPhones and Mobile Phones, oh my!".
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