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SocialTrust: Tamper-Resilient Trust Establishment in Online Communities
"... Web 2.0 promises rich opportunities for information sharing, electronic commerce, and new modes of social interaction, all centered around the“social Web”of user-contributed content, social annotations, and person-to-person social connections. But the increasing reliance on this “social Web ” also p ..."
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Cited by 23 (4 self)
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Web 2.0 promises rich opportunities for information sharing, electronic commerce, and new modes of social interaction, all centered around the“social Web”of user-contributed content, social annotations, and person-to-person social connections. But the increasing reliance on this “social Web ” also places individuals and their computer systems at risk, creating opportunities for malicious participants to exploit the tight social fabric of these networks. With these problems in mind, we propose the SocialTrust framework for tamperresilient trust establishment in online communities. Social-Trust provides community users with dynamic trust values by (i) distinguishing relationship quality from trust; (ii) incorporating a personalized feedback mechanism for adapting as the community evolves; and (iii) tracking user behavior. We experimentally evaluate the SocialTrust framework using real online social networking data consisting of millions of MySpace profiles and relationships. We find that SocialTrust supports robust trust establishment even in the presence of large-scale collusion by malicious participants.
Comments-oriented document summarization: Understanding documents with readers’ feedback
- In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. SIGIR ’08. ACM
, 2008
"... Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web ..."
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Cited by 22 (1 self)
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Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, andmention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the three graphs are merged into a multirelation graph; (ii) tensor-based method, where the three graphs are used to construct a 3rd-order tensor. To generate a comments-oriented summary, we extract sentences from the given Web document using either feature-biased approach or uniform-document approach. The former scores sentences to bias keywords derived from comments; while the latter scores sentences uniformly with comments. In our experiments using a set of blog posts with manually labeled sentences, our proposed summarization methods utilizing comments showed significant improvement over those not using comments. The methods using feature-biased sentence extraction approach were observed to outperform that using uniform-document approach.
Challenges in searching online communities
- IEEE Data Eng. Bull
"... An ever-growing number of users participate in online communities such as Flickr, del.icio.us, and YouTube, making friends and sharing content. Users come to these sites to find out about general trends – the most popular tags, or the most recently tagged item – as well as for more specific informat ..."
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Cited by 21 (0 self)
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An ever-growing number of users participate in online communities such as Flickr, del.icio.us, and YouTube, making friends and sharing content. Users come to these sites to find out about general trends – the most popular tags, or the most recently tagged item – as well as for more specific information, such as the recent posts of one of their friends. While these activities correspond to different user needs, they all can be seen as the filtering of resources in communities by various search criteria. We provide a survey of these search tasks and discuss the challenges in their efficient and effective evaluation. 1
Social search and discovery using a unified approach
- In Proceedings of HyperText
"... We explore new ways of improving a search engine using data from Web 2.0 applications such as blogs and social bookmarks. This data contains entities such as documents, people and tags, and relationships between them. We propose a simple yet effective method, based on faceted search, that treats all ..."
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Cited by 18 (5 self)
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We explore new ways of improving a search engine using data from Web 2.0 applications such as blogs and social bookmarks. This data contains entities such as documents, people and tags, and relationships between them. We propose a simple yet effective method, based on faceted search, that treats all entities in a unified manner: returning all of them (documents, people and tags) on every search, and allowing all of them to be used as search terms. We describe an implementation of such a social search engine on the intranet of a large enterprise, and present large-scale experiments which verify the validity of our approach.
Connecting Users and Items with Weighted Tags for Personalized Item Recommendations
- In Proc. of HT’10
"... This is the author’s version of a work that was submitted/accepted for pub-lication in the following source: ..."
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Cited by 17 (7 self)
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This is the author’s version of a work that was submitted/accepted for pub-lication in the following source:
Personalizing Web Search with Folksonomy-Based User and Document
- Profiles, in Advances in Information Retrieval. 2010, Springer Berlin
"... Abstract. Web search personalization aims to adapt search results to a user based on his tastes, interests and needs. The way in which such personal preferences are captured, modeled and exploited distinguishes the different personalization strategies. In this paper, we propose to represent a user p ..."
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Abstract. Web search personalization aims to adapt search results to a user based on his tastes, interests and needs. The way in which such personal preferences are captured, modeled and exploited distinguishes the different personalization strategies. In this paper, we propose to represent a user profile in terms of social tags, manually provided by users in folksonomy systems to describe, categorize and organize items of interest, and investigate a number of novel techniques that exploit the users ‟ social tags to re-rank results obtained with a Web search engine. An evaluation conducted with a dataset from Delicious social bookmarking system shows that our personalization techniques clearly outperform state of the art approaches. 1
Exploitation of semantic relationships and hierarchical data structures to support a user in his annotation and browsing . . .
, 2009
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User tags versus expert-assigned subject terms: A comparison of LibraryThing tags and library of congress subject headings
- Journal of Information Science
, 2010
"... Abstract. Social tagging, as a recent approach for creating metadata, has caught the attention of library and information science researchers. Many researchers recommend incorporating social tagging into the library environment and combining folksonomies with formal classification. However, some re ..."
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Cited by 15 (0 self)
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Abstract. Social tagging, as a recent approach for creating metadata, has caught the attention of library and information science researchers. Many researchers recommend incorporating social tagging into the library environment and combining folksonomies with formal classification. However, some researchers are concerned with the quality issues of social annotation because of its uncontrolled nature. In this study, we compare social tags created by users from the LibraryThing website with the subject terms assigned by experts according to the Library of Congress Subject Headings (LCSH). The purpose of this study is to examine the difference and connections between social tags and expert-assigned subject terms and further explore the feasibility and obstacles of implementing social tagging in library systems. The results of our study show that it is possible to use social tags to improve the accessibility of library collections. However, the existence of non-subject-related tags may impede the application of social tagging in traditional library cataloguing systems.
A Probabilistic Model for Personalized Tag Prediction
- PROCEEDINGS OF THE 16TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
, 2010
"... Social tagging systems have become increasingly popular for sharing and organizing web resources. Tag recommendation is a common feature of social tagging systems. Social tagging by nature is an incremental process, meaning that once a user has saved a web page with tags, the tagging system can prov ..."
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Cited by 15 (3 self)
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Social tagging systems have become increasingly popular for sharing and organizing web resources. Tag recommendation is a common feature of social tagging systems. Social tagging by nature is an incremental process, meaning that once a user has saved a web page with tags, the tagging system can provide more accurate predictions for the user, based on the user’s incremental behavior. However, existing tag prediction methods do not consider this important factor, in which their training and test datasets are either split by a fixed time stamp or randomly sampled from a larger corpus. In our temporal experiments, we perform a time-sensitive sampling on an existing public dataset, resulting in a new scenario which is much closer to “real-world”. In this paper, we address the problem of tag prediction by proposing a probabilistic model for personalized tag prediction. The model is a Bayesian approach, and integrates three factors— an ego-centric effect, environmental effects and web page content. Two methods—both intuitive calculation and learning optimization—are provided for parameter estimation. Pure graphbased methods which may have significant constraints (such as every user, every item and every tag has to occur in at least p posts) cannot make a prediction in most “real world ” cases while our model improves the F-measure by over 30 % compared to a leading algorithm on a publicly-available real-world dataset.
Socialsearchbrowser: A novel mobile search and information discovery tool
- In Proceedings of IUI’10. ACM
, 2010
"... The mobile Internet offers anytime, anywhere access to a wealth of information to billions of users across the globe. However, the mobile Internet represents a challenging information access platform due to the inherent limitations of mobile environments, limitations that go beyond simple screen siz ..."
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Cited by 13 (2 self)
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The mobile Internet offers anytime, anywhere access to a wealth of information to billions of users across the globe. However, the mobile Internet represents a challenging information access platform due to the inherent limitations of mobile environments, limitations that go beyond simple screen size and network issues. Mobile users often have information needs which are impacted by contexts such as location and time. Furthermore, human beings are social creatures that often seek out new strategies for sharing knowledge and information in mobile settings. To investigate the social aspect of mobile search, we have developed SocialSearch-Browser (SSB), a novel proof-of-concept interface that incorporates social networking capabilities with key mobile contexts to improve the search and information discovery experience of mobile users. In this paper, we present the results of an exploratory field study of SSB and outline key implications for the design of next generation mobile information access services. Author Keywords Mobile search, social search, social networks, location-based services, context, field study, user evaluation
