Search (7 results, page 1 of 1)

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Abstract

Many free mobile applications are supported by advertising. Ads can greatly affect user perceptions and behavior. In mobile apps, ads often follow a "native" format: they are designed to conform in both format and style to the actual content and context of the application. Clicking on the ad leads users to a second destination, outside of the hosting app, where the unified experience provided by native ads within the app is not necessarily reflected by the landing page the user arrives at. Little is known about whether and how this type of mobile ads is impacting user experience. In this paper, we use both quantitative and qualitative methods to study the impact of two design decisions for the landing page of a native ad on the user experience: (i) native ad style (following the style of the application) versus a non-native ad style; and (ii) pages with multimedia versus static pages. We found considerable variability in terms of user experience with mobile ad landing pages when varying presentation style and multimedia usage, especially interaction between presence of video and ad style (native or non-native). We also discuss insights and recommendations for improving the user experience with mobile native ads.

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Abstract

Documents often display a structure, e.g. several sections, each with several subsections and so on. Taking into account the structure of a document allows the retrieval process to focus on those parts of the document that are most relevant to an information need. In previous work, we developed a model for the representation and the retrieval of structured documents. This paper reports the first experimental study of the effectiveness and applicability of the model

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Footnote

This work was done while Janette Lehmann was a PhD student at Universitat Pompeu Fabra and it was carried out as part of her PhD internship at Yahoo! Labs Barcelona. This work was carried out while Carlos Castillo was working at Qatar Computing Research Institute.

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Abstract

Information is and always has been an elusive concept; nevertheless many philosophers, mathematicians, logicians and computer scientists have felt that it is fundamental. Many attempts have been made to come up with some sensible and intuitively acceptable definition of information; up to now, none of these have succeeded. This work is based on the approach followed by Dretske, Barwise, and Devlin, who claimed that the notion of information starts from the position that given an ontology of objects individuated by a cognitive agent, it makes sense to speak of the information an object (e.g., a text, an image, a video) contains about another object (e.g. the query). This phenomenon is captured by the flow of information between objects. Its exploitation is the task of an information retrieval system. These authors proposes a theory of information that provides an analysis of the concept of information (any type, from any media) and the manner in which intelligent organisms (referring to as cognitive agents) handle and respond to the information picked up from their environment. They defined the nature of information flow and the mechanisms that give rise to such a flow. The theory, which is based on Situation Theory, is expressed with a calculus defined on channels. The calculus was defined so that it satisfies properties that are attributes to information and its flows. This paper demonstrates the connection between this calculus and information retrieval, and porposes a model of an information retrieval system based on this calculus

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Abstract

Ruthven, Mounia, and van Rijsbergen rank and select terms for query expansion using information gathered on searcher evaluation behavior. Using the TREC Financial Times and Los Angeles Times collections and search topics from TREC-6 placed in simulated work situations, six student subjects each preformed three searches on an experimental system and three on a control system with instructions to search by natural language expression in any way they found comfortable. Searching was analyzed for behavior differences between experimental and control situations, and for effectiveness and perceptions. In three experiments paired t-tests were the analysis tool with controls being a no relevance feedback system, a standard ranking for automatic expansion system, and a standard ranking for interactive expansion while the experimental systems based ranking upon user information on temporal relevance and partial relevance. Two further experiments compare using user behavior (number assessed relevant and similarity of relevant documents) to choose a query expansion technique against a non-selective technique and finally the effect of providing the user with knowledge of the process. When partial relevance data and time of assessment data are incorporated in term ranking more relevant documents were recovered in fewer iterations, however retrieval effectiveness overall was not improved. The subjects, none-the-less, rated the suggested terms as more useful and used them more heavily. Explanations of what the feedback techniques were doing led to higher use of the techniques.

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Abstract

Prominent news sites on the web provide hundreds of news articles daily. The abundance of news content competing to attract online attention, coupled with the manual effort involved in article selection, necessitates the timely prediction of future popularity of these news articles. The future popularity of a news article can be estimated using signals indicating the article's penetration in social media (e.g., number of tweets) in addition to traditional web analytics (e.g., number of page views). In practice, it is important to make such estimations as early as possible, preferably before the article is made available on the news site (i.e., at cold start). In this paper we perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News, a major news provider. We characterize the popularity of news articles through a set of online metrics and try to predict their values across time using machine learning techniques on a large collection of features obtained from various sources. Our findings indicate that predicting news popularity at cold start is a difficult task, contrary to the findings of a prior work on the same topic. Most articles' popularity may not be accurately anticipated solely on the basis of content features, without having the early-stage popularity values.

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Date

22. 3.2003 19:27:36