Matches in ScholarlyData for { ?s ?p ?o. }
- 668 keyword "Location privacy framework".
- 668 title "Supporting Anonymous Location Queries in Mobile Environments with PrivacyGrid".
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- item-1 hasContent bhuvan-bamba.
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- item-2 hasContent ling-liu.
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- item-3 hasContent peter-pesti.
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- item-4 hasContent ting-wang.
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- 673 creator abraham-flaxman.
- 673 creator adam-kalai.
- 673 creator christian-borgs.
- 673 creator jennifer-chayes.
- 673 creator moshe-tennenholtz.
- 673 creator reid-andersen.
- 673 creator uriel-feige.
- 673 creator vahab-mirrokni.
- 673 type InProceedings.
- 673 label "Trust-based recommendation systems: an axiomatic approach".
- 673 sameAs 673.
- 673 abstract "High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommendations may incorporate this information. This paper focuses on networks which represent trust and recommendations which incorporate trust relationships. The goal of a trust-based recommendation system is to generate personalized recommendations from known opinions and trust relationships. In analogy to prior work on voting and ranking systems, we use the axiomatic approach from the theory of social choice. We develop an natural set of five axioms which we desire any recommendation system exhibit. Then we show that no system can simultaneously satisfy all these axioms. We also exhibit systems which satisfy any four of the five axioms. Next we consider ways of weakening the axioms, which can lead to a unique recommendation system based on random walks. We consider other recommendation systems (personal page rank, majority of majorities, and min cut) and search for alternative axiomatizations which uniquely characterize these systems. Finally, we determine which of these systems are incentive compatible. This is an important property for systems deployed in a monetized environment: groups of agents interested in manipulating recommendations to make others share their opinion have nothing to gain from lying about their votes or their trust links.".
- 673 hasAuthorList authorList.
- 673 hasTopic World_Wide_Web.
- 673 isPartOf proceedings.
- 673 keyword "Axiomatic approach".
- 673 keyword "Recommendation systems".
- 673 keyword "Reputation systems".
- 673 keyword "Trust networks".
- 673 title "Trust-based recommendation systems: an axiomatic approach".
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- item-1 hasContent reid-andersen.
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- item-2 hasContent christian-borgs.
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- item-3 hasContent jennifer-chayes.
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- item-4 hasContent uriel-feige.
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- item-5 hasContent abraham-flaxman.
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- item-6 hasContent adam-kalai.
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- item-7 hasContent vahab-mirrokni.
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- item-8 hasContent moshe-tennenholtz.
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- 694 creator bin-swen.
- 694 creator honglei-guo.
- 694 creator qi-su.
- 694 creator xian-wu.
- 694 creator xiaoxun-zhang.
- 694 creator xinying-xu.
- 694 creator zhili-guo.
- 694 creator zhong-su.
- 694 type InProceedings.
- 694 label "Hidden Sentiment Association in Chinese Web Opinion Mining".
- 694 sameAs 694.
- 694 abstract "The boom of product review websites, blogs and forums on the web attracts many research efforts on opinion mining. Recently, there was a growing interest in the finer-grained opinion mining, which detects opinions on different review features instead of the whole review level. The researches on feature-level opinion mining mainly rely on identifying the explicit relatedness between product feature words and opinion words in reviews. However, the sentiment relatedness between the two objects is usually complicated. For many cases, product feature words are implied by the opinion words in reviews. The detection of such hidden sentiment association is still a big challenge in opinion mining. Especially, it is an even harder task of feature-level opinion mining on Chinese reviews due to the nature of Chinese language. In this paper, we propose a novel mutual reinforcement approach to deal with the feature-level opinion mining problem. More specially, 1) the approach clusters product features and opinion words simultaneously and iteratively by fusing both their content information and sentiment link information. 2) under the same framework, based on the product feature categories and opinion word groups, we construct the sentiment association set between the two groups of data objects by identifying their strongest n sentiment links. Moreover, knowledge from multi-source is incorporated to enhance clustering in the procedure. Based on the pre-constructed association set, our approach can largely predict opinions relating to different product features, even for the case without explicit appearance of product feature words in reviews. Thus it provides a more accurate opinion evaluation. The experimental results demonstrate that our method outperforms the state-of-art algorithms.".
- 694 hasAuthorList authorList.
- 694 hasTopic World_Wide_Web.
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- 694 keyword "association".