BibSonomy publications for /tag/SocialSearchTue Jan 04 14:22:10 CET 2011New York, NY, USAThe 20th ACM conference on Hypertext and hypermedia (HT '09)July271--278Cross-Tagging for Personalized Open Social Networking2009CrossTagging SocialSearch collaborative-filtering myown The Social Web is successfully established and poised for continued growth. Web 2.0 applications such as blogs, bookmarking, music, photo and video sharing systems are among the most popular; and all of them incorporate a social aspect, i.e., users can easily share information with other users. But due to the diversity of these applications -- serving different aims -- the Social Web is ironically divided. Blog users who write about music for example, could possibly benefit from other users registered in other social systems operating within the same domain, such as a social radio station. Although these sites are two different and disconnected systems, offering distinct services to the users, the fact that domains are compatible could benefit users from both systems with interesting and multi-faceted information. In this paper we propose to automatically establish social links between distinct social systems through cross-tagging, i.e., enriching a social system with the tags of other similar social system(s). Since tags are known for increasing the prediction quality of recommender systems (RS), we propose to quantitatively evaluate the extent to which users can benefit from cross-tagging by measuring the impact of different cross-tagging approaches on tag-aware RS for personalized resource recommendations. We conduct experiments in real world data sets and empirically show the effectiveness of our approaches. Sat Apr 10 15:43:57 CEST 2010New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining931-940iLink: search and routing in social networks2007expertidenti?cation iLink Experimentation smartRSS web2.0 socialsearch peerproduction Algorithms messagerouting,learning socialFAQgeneration The growth of Web 2.0 and fundamental theoretical breakthroughs have led to an avalanche of interest in social networks. This paper focuses on the problem of modeling how social networks accomplish tasks through peer production style collaboration. We propose a general interaction model for the underlying social networks and then a specific model (iLink for social search and message routing. A key contribution here is the development of a general learning framework for making such online peer production systems work at scale. The iLink model has been used to develop a system for FAQ generation in a social network (FAQtory), and experience with its application in the context of a full-scale learning-driven workflow application (CALO) is reported. We also discuss methods of adapting iLink technology for use in military knowledge sharing portals and other message routing systems. Finally, the paper shows the connection of iLink to SQM, a theoretical model for social search that is a generalization of Markov Decision Processes and the popular Pagerank model.Wed Nov 18 16:32:06 CET 2009New York, NY, USACHI EA '09: Proceedings of the 27th international conference extended abstracts on Human factors in computing systems3377--3382Exploring the cognitive consequences of social search2009SearchInterface SocialSearch To what extent can social interactions augment people's natural search experiences? What factors influence the decision to turn to a friend for help? Our paper presents the preliminary results of a social sensemaking task that begin to address such questions by examining the cognitive consequences of social search.Wed Oct 21 12:58:58 CEST 2009New York, NY, USAHT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia219--228RichVSM: enRiched vector space models for folksonomies2009IR SocialSearch folksonomy ranking People share millions of resources (photos, bookmarks, videos, etc.) in Folksonomies (like Flickr, Delicious, Youtube, etc.). To access and share resources, they add keywords called tags to the resources. As the tags are freely chosen keywords, it might not be possible for users to tag their resources with all the relevant tags. As a result, many resources lack sufficient number of relevant tags. The lack of relevant tags results into sparseness of data, and this sparseness of data makes many relevant resources unsearchable against user queries.Thu Oct 08 15:31:12 CEST 2009New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web943--952Towards effective browsing of large scale social annotations2007SocialSearch browsing This paper is concerned with the problem of browsing social annotations. Today, a lot of services (e.g., Del.icio.us, Filckr) have been provided for helping users to manage and share their favorite URLs and photos based on social annotations. Due to the exponential increasing of the social annotations, more and more users, however, are facing the problem how to effectively find desired resources from large annotation data. Existing methods such as tag cloud and annotation matching work well only on small annotation sets. Thus, an effective approach for browsing large scale annotation sets and the associated resources is in great demand by both ordinary users and service providers. In this paper, we propose a novel algorithm, namely Effective Large Scale Annotation Browser (ELSABer), to browse large-scale social annotation data. ELSABer helps the users browse huge number of annotations in a semantic, hierarchical and efficient way. More specifically, ELSABer has the following features: 1) the semantic relations between annotations are explored for browsing of similar resources; 2) the hierarchical relations between annotations are constructed for browsing in a top-down fashion; 3) the distribution of social annotations is studied for efficient browsing. By incorporating the personal and time information, ELSABer can be further extended for personalized and time-related browsing. A prototype system is implemented and shows promising results.Thu Oct 08 15:25:56 CEST 2009New York, NY, USACHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems1657--1660A survey of collaborative web search practices2008SocialSearch Today's Web browsers provide limited support for rich information-seeking and information-sharing scenarios. A survey we conducted of 204 knowledge workers at a large technology company has revealed that a large proportion of users engage in searches that include collaborative activities. We present the results of the survey, and then review the implications of these findings for designing new Web search interfaces that provide tools for sharing.Thu Oct 08 15:22:09 CEST 2009Proceedings of ECIR'08 Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR 2008)3Introducing Triple Play for Improved Resource Retrieval in Collaborative Tagging Systems2008SocialSearch folksonomy tagging Collaborative tagging systems (like Flickr, del.icio.us, citeulike, etc.) are becoming more popular with passage of time. Users share their resources on tagging systems, and add keywords (called tags) to these resources. Users can search resources using these tags. But as the user gives more tags for search, he might not get sufficient search results, because the resources might not be tagged with all the related tags. We introduce the method Triple Play, which smoothes the tag space by user space for improved retrieval of resources. As a part of Triple Play, we also propose two new vector space models for collaborative tagging systems, SmoothVSM Dense and SmoothVSM Sparse. These vector space models exploit the user-tag co-occurrence relationship to overcome the problem of missing information in tagging systems. Finally we apply Latent Semantic Analysis to different vector space models and analyze the results. Initial experimentation show that using additional information available in tagging systems helps in improving search in tagging systems.Thu Oct 08 15:19:42 CEST 2009Proc. VLDB Endow.1710--721Efficient network aware search in collaborative tagging sites12008SocialSearch folksonomy tagging The popularity of collaborative tagging sites presents a unique opportunity to explore keyword search in a context where query results are determined by the opinion of a network of taggers related to a seeker. In this paper, we present the first in-depth study of network-aware search. We investigate efficient top-k processing when the score of an answer is computed as its popularity among members of a seeker's network. We argue that obvious adaptations of top-k algorithms are too space-intensive, due to the dependence of scores on the seeker's network. We therefore develop algorithms based on maintaining score upper-bounds. The global upper-bound approach maintains a single score upper-bound for every pair of item and tag, over the entire collection of users. The resulting bounds are very coarse. We thus investigate clustering seekers based on similar behavior of their networks. We show that finding the optimal clustering of seekers is intractable, but we provide heuristic methods that give substantial time improvements. We then give an optimization that can benefit smaller populations of seekers based on clustering of taggers. Our results are supported by extensive experiments on del.icio.us datasets.Fri Oct 02 18:02:33 CEST 2009New York, NY, USASIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval523--530Efficient top-k querying over social-tagging networks2008SocialSearch Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances.Fri Oct 02 18:01:14 CEST 2009New York, NY, USAIUI '07: Proceedings of the 12th international conference on Intelligent user interfaces282--285On the community-based explanation of search results2007SocialSearch Collaborative Web search (CWS) is an approach to personalizing search results, returned by an underlying search engine(s), to the preferences of a community of like-minded searchers. In this paper we propose an alternative architecture that facilitates a more flexible integration between CWS and the underlying search engine(s) and evaluate how community behaviour can be used to annotate search results with explanatory information to facilitate relevancy judgments.Fri Oct 02 17:57:48 CEST 2009Information Processing & Management41562--1579Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web442008SocialSearch folksonomy tagging Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.Tue Sep 01 11:18:31 CEST 2009New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web501--510Optimizing web search using social annotations2007SocialSearch WebSearch folksonomy tagging Tue Aug 25 15:51:11 CEST 2009HeidelbergThe Semantic Web: Research and ApplicationsJune411-426LNAIInformation Retrieval in Folksonomies: Search and Ranking40112006SocialSearch folksonomy Wed Jul 22 15:46:19 CEST 2009New York, NY, USAJCDL '09: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries163--172EnTag: enhancing social tagging for discovery2009SocialSearch tagging The EnTag (Enhanced Tagging for Discovery) project investigated the effect on indexing and retrieval when using only social tagging versus when using social tagging in combination with suggestions from a controlled vocabulary. Two different contexts were explored: tagging by readers of a digital collection and tagging by authors in an institutional repository; also two different controlled vocabularies were examined, Dewey Decimal Classification and ACM Computing Classification Scheme. For each context a separate demonstrator was developed and a user study conducted. The results showed the importance of controlled vocabulary suggestions for both indexing and retrieval: to help produce ideas of tags to use, to make it easier to find focus for the tagging, as well as to ensure consistency and increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both in terms of conceptual relevance to the user and in appropriateness of the terminology. The participants themselves could also see the advantages of controlled vocabulary terms for retrieval if the terms used were from an authoritative source.Wed Jul 15 11:30:21 CEST 2009New York, NY, USAWWW '09: Proceedings of the 18th international conference on World wide web641--650Evaluating similarity measures for emergent semantics of social tagging2009SocialSearch evaluation similarity tagging Mon Jul 13 16:41:19 CEST 2009San Jose, CA, USAInt'l AAAI Conference on Weblogs and Social Media (ICWSM)MayPersonal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems2009SocialSearch folksonomy tagging Mon Jul 06 11:42:26 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulySocial Recommender Systems for Web 2.0 Folksonomies2009RecommenderSystems SocialSearch social The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.Mon Jul 06 11:40:17 CEST 2009New York, NY, USAHT '09: Proceedings of the Twentieth ACM Conference on Hypertext and HypermediaJulyCross-Tagging for Personalized Open Social Networking2009CrossTagging SocialSearch tagging The Social Web is successfully established and poised for continued growth. Web 2.0 applications such as blogs, bookmarking, music, photo and video sharing systems are among the most popular; and all of them incorporate a social aspect, i.e., users can easily share information with other users. But due to the diversity of these applications -- serving different aims -- the Social Web is ironically divided. Blog users who write about music for example, could possibly benefit from other users registered in other social systems operating within the same domain, such as a social radio station. Although these sites are two different and disconnected systems, offering distinct services to the users, the fact that domains are compatible could benefit users from both systems with interesting and multi-faceted information. In this paper we propose to automatically establish social links between distinct social systems through cross-tagging, i.e., enriching a social system with the tags of other similar social system(s). Since tags are known for increasing the prediction quality of recommender systems (RS), we propose to quantitatively evaluate the extent to which users can benefit from cross-tagging by measuring the impact of different cross-tagging approaches on tag-aware RS for personalized resource recommendations. We conduct experiments in real world data sets and empirically show the effectiveness of our approaches. Mon Jul 06 11:34:28 CEST 2009New York, NY, USAHT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia219--228RichVSM: enRiched vector space models for folksonomies2009InformationRetrieval SocialSearch folksonomy similarity People share millions of resources (photos, bookmarks, videos, etc.) in Folksonomies (like Flickr, Delicious, Youtube, etc.). To access and share resources, they add keywords called tags to the resources. As the tags are freely chosen keywords, it might not be possible for users to tag their resources with all the relevant tags. As a result, many resources lack sufficient number of relevant tags. The lack of relevant tags results into sparseness of data, and this sparseness of data makes many relevant resources unsearchable against user queries.Mon Jul 06 11:24:41 CEST 2009New York, NY, USAHT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia199--208Social search and discovery using a unified approach2009SocialSearch This research explores new ways to augment the search and discovery of relations between Web 2.0 entities using multiple types and sources of social information. Our goal is to allow the search for all object types such as documents, persons and tags, while retrieving related objects of all types. We implemented a social-search engine using a unified approach, where the search space is expanded to represent heterogeneous information objects that are interrelated by several relation types. Our solution is based on multifaceted search, which provides an efficient update mechanism for relations between objects, as well as efficient search over the heterogeneous data. We describe a social search engine positioned within a large enterprise, applied over social data gathered from several Web 2.0 applications. We conducted a large user study with over 600 people to evaluate the contribution of social data for search. Our results demonstrate the high precision of social search results and confirm the strong relationship of users and tags to the topics retrieved.SocialSearchCommunity for tag(s) SocialSearch