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- 185 type InProceedings.
- 185 label "Identifying Vertical Search Intention of Query through Social Tagging Propagation".
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- 185 abstract "A pressing task during the unification process is to identify a user's vertical search intention based on the user's query. In this paper, we propose a novel method to propagate social annotation, which includes user-supplied tag data, to both queries and VSEs for semantically bridging them. Our proposed algorithm consists of three key steps: query annotation, vertical annotation and query intention identification. Our algorithm, referred to as TagQV, verifies that the social tagging can be propagated to represent Web objects such as queries and VSEs besides Web pages. Experiments on real Web search queries demonstrate the effectiveness of TagQV in query intention identification.".
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- 185 title "Identifying Vertical Search Intention of Query through Social Tagging Propagation".
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- 186 type InProceedings.
- 186 label "Social Search and Discovery Using a Unified Approach".
- 186 sameAs 186.
- 186 abstract "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.".
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- 186 title "Social Search and Discovery Using a Unified Approach".
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- item-1 hasContent einat-amitay.
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- 187 type InProceedings.
- 187 label "Extracting Community Structure through Relational Hypergraphs".
- 187 sameAs 187.
- 187 abstract "Social media websites promote diverse user interaction on media objects as well as user actions with respect to other users. The goal of this work is to discover community structure in rich media social networks, and observe how it evolves over time, through analysis of multi-relational data. The problem is important in the enterprise domain where extracting emergent community structure on enterprise social media, can help in forming new collaborative teams, aid in expertise discovery, and guide long term enterprise reorganization. Our approach consists of three main parts: (1) a relational hypergraph model for modeling various social context and interactions; (2) a novel hypergraph factorization method for community extraction on multi-relational social data; (3) an online method to handle temporal evolution through incremental hypergraph factorization. Extensive experiments on real-world enterprise data suggest that our technique is scalable and can extract meaningful communities. To evaluate the quality of our mining results, we use our method to predict users' future interests. Our prediction outperforms baseline methods (frequency counts, pLSA) by 36-250% on the average, indicating the utility of leveraging multi-relational social context by using our method.".
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- 187 title "Extracting Community Structure through Relational Hypergraphs".
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