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• item-7 hasContent jiajun-bu.
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• item-8 type ListItem.
• item-8 hasContent chun-chen.
• item-8 previous item-7.
• 129 type InProceedings.
• 129 label "GameSense".
• 129 sameAs 129.
• 129 abstract "This paper presents a novel game-like advertising system called GameSense, which is driven by the compelling contents of online images. Given a Web page which typically contains images, GameSense is able to select suitable images to create online in-image games for advertising. The contextually relevant ads (i.e., product logos) are embedded at appropriate positions within the online games. The ads are selected based on not only textual relevance but also visual content similarity. The game is able to provide viewers rich experience and thus promote the embedded ads to provide more effective advertising.".
• 129 hasAuthorList authorList.
• 129 isPartOf proceedings.
• 129 keyword "Poster Session".
• 129 title "GameSense".
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• item-1 type ListItem.
• item-1 hasContent lusong-li.
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• item-2 type ListItem.
• item-2 hasContent tao-mei.
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• item-3 type ListItem.
• item-3 hasContent chris-liu.
• item-3 next item-4.
• item-3 previous item-2.
• item-4 type ListItem.
• item-4 hasContent xian-sheng-hua.
• item-4 previous item-3.
• 13 type InProceedings.
• 13 label "Learning Consensus Opinion: Mining Data from a Labeling Game".
• 13 sameAs 13.
• 13 abstract "We consider the problem of identifying the consensus rank- ing for the results of a query, given preferences among those results from a set of individual users. Once consensus rank- ings are identified for a set of queries, these rankings can serve for both evaluation and training of retrieval and learn- ing systems. We present a novel approach to collecting the individual user preferences over image-search results: we use a collaborative game in which players are rewarded for agree- ing on which image result is best for a query. Our approach is distinct from other labeling games because we are able to elicit directly the preferences of interest with respect to image queries extracted from query logs. As a source of rel- evance judgments, this data provides a useful complement to click data. Furthermore, the data is free of positional biases and is collected by the game without the risk of frus- trating users with non-relevant results; this risk is prevalent in standard mechanisms for debiasing clicks. We describe data collected over 34 days from a deployed version of this game that amounts to about 18 million expressed prefer- ences between pairs. Finally, we present several approaches to modeling this data in order to extract the consensus rank- ings from the preferences and better sort the search results for targeted queries.".
• 13 hasAuthorList authorList.
• 13 isPartOf proceedings.
• 13 keyword "Data Mining".
• 13 title "Learning Consensus Opinion: Mining Data from a Labeling Game".
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• item-1 type ListItem.
• item-1 hasContent paul-n-bennett.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent david-maxwell-chickering.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent anton-mityagin.
• item-3 previous item-2.
• 130 type InProceedings.
• 130 label "Rare Item Detection in e-Commerce Site".
• 130 sameAs 130.
• 130 abstract "As the largest online marketplace in the world, eBay has a huge inventory where there are plenty of great rare items with potentially large, even rapturous buyers. These items are obscured in long tail of eBay item listing and hard to find through existing searching or browsing methods. It is observed that there are great rarity demands from users according to eBay query log. To keep up with the demands, the paper proposes a method to automatically detect rare items in eBay online listing. A large set of features relevant to the task are investigated to filter items and further measure item rareness. The experiments on the most rarity-demandintensitive domains show that the method may effectively detect rare items (> 90% precision).".
• 130 hasAuthorList authorList.
• 130 isPartOf proceedings.
• 130 keyword "Poster Session".
• 130 title "Rare Item Detection in e-Commerce Site".
• authorList type List.
• authorList hasFirstItem item-1.
• authorList hasItem item-1.
• authorList hasItem item-2.
• authorList hasItem item-3.
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• item-1 type ListItem.
• item-1 hasContent dan-shen.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent xiaoyuan-wu.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent alvaro-bolivar.
• item-3 previous item-2.
• 131 type InProceedings.
• 131 label "A Declarative Framework for Semantic Link Discovery over Relational Data".
• 131 sameAs 131.
• 131 abstract "In this paper, we present a framework for online discovery of semantic links from relational data. Our framework is based on declarative specification of the linkage require- ments by the user, that allows matching data items in many real-world scenarios. These requirements are translated to queries that can run over the relational data source, potentially using the semantic knowledge to enhance the accuracy of link discovery. Our framework lets data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.".
• 131 hasAuthorList authorList.
• 131 isPartOf proceedings.
• 131 keyword "Poster Session".
• 131 title "A Declarative Framework for Semantic Link Discovery over Relational Data".
• authorList type List.
• authorList hasFirstItem item-1.
• authorList hasItem item-1.
• authorList hasItem item-2.
• authorList hasItem item-3.
• authorList hasItem item-4.
• authorList hasLastItem item-4.
• item-1 type ListItem.
• item-1 hasContent oktie-hassanzadeh.

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