Matches in ScholarlyData for { ?s ?p ?o. }
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasItem item-5.
- authorList hasItem item-6.
- authorList hasLastItem item-6.
- item-1 type ListItem.
- item-1 hasContent sanjay-agrawal.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent kaushik-chakrabarti.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent surajit-chaudhuri.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent venkatesh-ganti.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent arnd-christian-koenig.
- item-5 next item-6.
- item-5 previous item-4.
- item-6 type ListItem.
- item-6 hasContent dong-xin.
- item-6 previous item-5.
- 52 type InProceedings.
- 52 label "Online Expansion of Rare Queries for Sponsored Search".
- 52 sameAs 52.
- 52 abstract "Sponsored search systems are tasked with matching queries to relevant advertisements. The current state-of-the-art matching algorithms expand the user's query using a variety of external resources, such as Web search results. While these expansion-based algorithms are highly effective, they are largely inefficient and cannot be applied in real-time. In practice, such algorithms are applied offline to popular queries, with the results of the expensive operations cached for fast access at query time. In this paper, we describe an efficient and effective approach for matching ads against rare queries that were not processed offline. The approach builds an expanded query representation by leveraging offline processing done for related popular queries. Our experimental results show that our approach significantly improves the effectiveness of advertising on rare queries with only a negligible increase in computational cost.".
- 52 hasAuthorList authorList.
- 52 isPartOf proceedings.
- 52 keyword "Search".
- 52 title "Online Expansion of Rare Queries for Sponsored Search".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasItem item-5.
- authorList hasItem item-6.
- authorList hasItem item-7.
- authorList hasLastItem item-7.
- item-1 type ListItem.
- item-1 hasContent andrei-broder.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent peter-ciccolo.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent evgeniy-gabrilovich.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent vanja-josifovski.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent donald-metzler.
- item-5 next item-6.
- item-5 previous item-4.
- item-6 type ListItem.
- item-6 hasContent lance-riedel.
- item-6 next item-7.
- item-6 previous item-5.
- item-7 type ListItem.
- item-7 hasContent jeffrey-yuan.
- item-7 previous item-6.
- 53 type InProceedings.
- 53 label "Collective Privacy Management in Social Networks".
- 53 sameAs 53.
- 53 abstract "Social Networking is one of the major technological phe- nomena of the Web 2.0, with hundreds of millions of people participating. Social networks enable a form of self expres- sion for users, and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites, Social Networks yet do not support any mech- anism for collaborative management of privacy settings for shared content. In this paper, we model the problem of collaborative enforcement of privacy policies on shared data by using game theory. In particular, we propose a solu- tion that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness, and that rewards users who pro- mote co-ownership. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been pro- posed. In the paper, we also show a proof-of-concept appli- cation, which we implemented in the context of Facebook, one of today's most popular social networks. We show that supporting these type of solutions is not also feasible, but can be implemented through a minimal increase in overhead to end-users.".
- 53 hasAuthorList authorList.
- 53 isPartOf proceedings.
- 53 keyword "Security and Privacy".
- 53 title "Collective Privacy Management in Social Networks".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent anna-c-squicciarini.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent mohamed-shehab.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent federica-paci.
- item-3 previous item-2.
- 54 type InProceedings.
- 54 label "To Join or Not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles".
- 54 sameAs 54.
- 54 abstract "In order to address privacy concerns, many social media websites allow users to hide their personal profiles from the public. In this work, we show how an adversary can exploit an online social network with a mixture of public and private user profiles to predict the private attributes of users. We map this problem to a relational classification problem and we propose practical models that use friendship and group membership information (which is often not hidden) to infer sensitive attributes. The key novel idea is that in addition to friendship links, groups can be carriers of significant information. We show that on several well-known social media sites, we can easily and accurately recover the information of private-profile users. To the best of our knowledge, this is the first work that uses link-based and group-based classification to study privacy implications in social networks with mixed public and private user profiles.".