Matches in ScholarlyData for { ?s ?p ?o. }
- 237 abstract "In an ideal world, interface design is the art and science of helping users accomplish tasks in a timely, efficient, and pleasurable manner. This paper studies the inverse situation, the vast emergence of deliberately constructed malicious interfaces that violate design best practices in order to accomplish goals counter to those of the user. This has become a commonplace occurrence both on and off the desktop, particularly on the web. The objective of this paper is to formally define this problem, including construction of a taxonomy of malicious interface techniques and an analysis of their impact on users. Findings are also presented on the self-reported tolerance and expectation levels of users with regard to malicious interfaces as well as on the effectiveness and ease of use of existing countermeasures. Our results were accomplished through significant compilation of malicious interface techniques based on review of thousands of web sites and by conducting three original surveys. Ultimately, this paper concludes that malicious interfaces are an ubiquitous problem that demands intervention by the security and human computer interaction communities in order to reduce the negative impact on the global user population and to call for a cultural shift in the advertising design community.".
- 237 hasAuthorList authorList.
- 237 isPartOf proceedings.
- 237 keyword "Design".
- 237 keyword "evaluation of web interactions".
- 237 title "Malicious Interface Design: Exploiting the User".
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- item-1 hasContent gregory-conti.
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- item-2 hasContent edward-sobiesk.
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- 254 creator wenzhao-tan.
- 254 creator xiao-li.
- 254 creator xiaoxin-yin.
- 254 creator yi-chin-tu.
- 254 type InProceedings.
- 254 label "Automatic Extraction of Clickable Structured Web Contents for Name Entity Queries".
- 254 sameAs 254.
- 254 abstract "Today the major web search engines answer queries by showing ten result snippets, which need to be inspected by users for identifying relevant results. In this paper we investigate how to extract structured information from the web, in order to directly answer queries by showing the contents being searched for. We treat users’ search trails (i.e., post-search browsing behaviors) as implicit labels on the relevance between web contents and user queries. Based on such labels we use information extraction approach to build wrappers and extract structured information. An important observation is that many web sites contain pages for name entities of certain categories (e.g., AOL Music contains a page for each musician), and these pages have the same format. This makes it possible to build wrappers from a small amount of implicit labels, and use them to extract structured information from many web pages for different name entities. We propose STRUCLICK, a fully automated system for extracting structured information for queries containing name entities of certain categories. It can identify important web sites from web search logs, build wrappers from users’ search trails, filter out bad wrappers built from random user clicks, and combine structured information from different web sites for each query. Comparing with existing approaches on information extraction, STRUCLICK can assign semantics to extracted data without any human labeling or supervision. We perform comprehensive experiments, which show STRUCLICK achieves high accuracy and good scalability.".
- 254 hasAuthorList authorList.
- 254 isPartOf proceedings.
- 254 keyword "Semantic search".
- 254 keyword "entity retrieval".
- 254 keyword "geo/temporal search".
- 254 keyword "sub/super-documents".
- 254 title "Automatic Extraction of Clickable Structured Web Contents for Name Entity Queries".
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- item-1 hasContent xiaoxin-yin.
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- item-2 hasContent wenzhao-tan.
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- item-3 hasContent xiao-li.
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- item-4 hasContent yi-chin-tu.
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- 256 creator alex-ntoulas.
- 256 creator livia-polanyi.
- 256 creator panayiotis-tsaparas.
- 256 creator yue-lu.
- 256 type InProceedings.
- 256 label "Exploiting Social Context for Review Quality Prediction".
- 256 sameAs 256.
- 256 abstract "Online reviews in which users publish detailed commentary on web portals about their experiences and opinions with products, services or events are extremely valuable to users who rely on them to make informed decisions. However, because reviews vary greatly in quality, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the review quality prediction problem by treating a review as a stand-alone text document, extracting features from the review text, and learning a function based on these features for predicting the review quality. In this work, we exploit contextual information about authors' identity and social networks on top of review text features for review quality prediction. We propose a generic framework for incorporating social context information by adding novel regularization constraints to the text-based predictor. Our approach can effectively use the social context information available for large amount of unlabeled reviews. It also has the advantage that the resulting predictor is usable even if social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social contextual information can help improve the accuracy of review quality prediction especially when the available training data is sparse.".
- 256 hasAuthorList authorList.
- 256 isPartOf proceedings.
- 256 keyword "Use of social metadata".
- 256 keyword "annotations in search".
- 256 keyword "mining".
- 256 title "Exploiting Social Context for Review Quality Prediction".
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- item-1 type ListItem.
- item-1 hasContent yue-lu.
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- item-2 hasContent panayiotis-tsaparas.
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- item-3 hasContent alex-ntoulas.
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- item-4 hasContent livia-polanyi.
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- 258 creator anand-shukla.
- 258 creator davood-rafiei.
- 258 creator krishna-bharat.
- 258 type InProceedings.
- 258 label "Diversifying Web Search Results".
- 258 sameAs 258.
- 258 abstract "Result diversity is a topic of great importance as more facets of queries are discovered and users expect to find their desired facets in the first page of the results. However, the underlying questions of how `diversity' interplays with `quality' and when preference should be given to one or both are not well-understood. In this work, we model the problem as expectation maximization and study the challenges of estimating the model parameters and reaching an equilibrium. One model parameter, for example, is correlations between pages which we estimate using textual contents of pages and click data (when available). We conduct experiments on diversifying randomly selected queries from a query log and the queries chosen from the disambiguation topics of Wikipedia. Our algorithm improves upon Google in terms of the diversity of random queries, retrieving 14% to 38% more aspects of queries in top 5, while maintaining a precision very close to Google. On a more selective set of queries that are expected to benefit from diversification, our algorithm improves upon Google in terms of precision and diversity of the results, and significantly outperforms another baseline system for result diversification.".
- 258 hasAuthorList authorList.
- 258 isPartOf proceedings.
- 258 keyword "Query processing".
- 258 keyword "diversity".
- 258 keyword "ranking".