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  • × author_ss:"Lalmas, M."
  1. Arapakis, I.; Cambazoglu, B.B.; Lalmas, M.: On the feasibility of predicting popular news at cold start (2017) 0.01
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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.
  2. Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Special issue on model design, formulation and explanation in information retrieval using mathematics (2006) 0.01
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  3. Lalmas, M.; Ruthven, I.: ¬A model for structured document retrieval : empirical investigations (1997) 0.01
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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
  4. Piwowarski, B.; Amini, M.R.; Lalmas, M.: On using a quantum physics formalism for multidocument summarization (2012) 0.01
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    Abstract
    Multidocument summarization (MDS) aims for each given query to extract compressed and relevant information with respect to the different query-related themes present in a set of documents. Many approaches operate in two steps. Themes are first identified from the set, and then a summary is formed by extracting salient sentences within the different documents of each of the identified themes. Among these approaches, latent semantic analysis (LSA) based approaches rely on spectral decomposition techniques to identify the themes. In this article, we propose a major extension of these techniques that relies on the quantum information access (QIA) framework. The latter is a framework developed for modeling information access based on the probabilistic formalism of quantum physics. The QIA framework not only points out the limitations of the current LSA-based approaches, but motivates a new principled criterium to tackle multidocument summarization that addresses these limitations. As a byproduct, it also provides a way to enhance the LSA-based approaches. Extensive experiments on the DUC 2005, 2006 and 2007 datasets show that the proposed approach consistently improves over both the LSA-based approaches and the systems that competed in the yearly DUC competitions. This demonstrates the potential impact of quantum-inspired approaches to information access in general, and of the QIA framework in particular.
  5. Rijsbergen, C.J. van; Lalmas, M.: Information calculus for information retrieval (1996) 0.01
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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
  6. Ruthven, T.; Lalmas, M.; Rijsbergen, K.van: Incorporating user research behavior into relevance feedback (2003) 0.01
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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.
  7. Lehmann, J.; Castillo, C.; Lalmas, M.; Baeza-Yates, R.: Story-focused reading in online news and its potential for user engagement (2017) 0.01
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    Abstract
    We study the news reading behavior of several hundred thousand users on 65 highly visited news sites. We focus on a specific phenomenon: users reading several articles related to a particular news development, which we call story-focused reading. Our goal is to understand the effect of story-focused reading on user engagement and how news sites can support this phenomenon. We found that most users focus on stories that interest them and that even casual news readers engage in story-focused reading. During story-focused reading, users spend more time reading and a larger number of news sites are involved. In addition, readers employ different strategies to find articles related to a story. We also analyze how news sites promote story-focused reading by looking at how they link their articles to related content published by them, or by other sources. The results show that providing links to related content leads to a higher engagement of the users, and that this is the case even for links to external sites. We also show that the performance of links can be affected by their type, their position, and how many of them are present within an article.
  8. Lalmas, M.; Ruthven, I.: Representing and retrieving structured documents using the Dempster-Shafer theory of evidence : modelling and evaluation (1998) 0.01
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    Abstract
    Reports on a theoretical model of structured document indexing and retrieval based on the Dempster-Schafer Theory of Evidence. Includes a description of the model of structured document retrieval, the representation of structured documents, the representation of individual components, how components are combined, details of the combination process, and how relevance is captured within the model. Also presents a detailed account of an implementation of the model, and an evaluation scheme designed to test the effectiveness of the model
  9. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.01
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    Date
    22. 3.2003 19:27:36
  10. Arapakis, I.; Lalmas, M.; Ceylan, H.; Donmez, P.: Automatically embedding newsworthy links to articles : from implementation to evaluation (2014) 0.01
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    Abstract
    News portals are a popular destination for web users. News providers are therefore interested in attaining higher visitor rates and promoting greater engagement with their content. One aspect of engagement deals with keeping users on site longer by allowing them to have enhanced click-through experiences. News portals have invested in ways to embed links within news stories but so far these links have been curated by news editors. Given the manual effort involved, the use of such links is limited to a small scale. In this article, we evaluate a system-based approach that detects newsworthy events in a news article and locates other articles related to these events. Our system does not rely on resources like Wikipedia to identify events, and it was designed to be domain independent. A rigorous evaluation, using Amazon's Mechanical Turk, was performed to assess the system-embedded links against the manually-curated ones. Our findings reveal that our system's performance is comparable with that of professional editors, and that users find the automatically generated highlights interesting and the associated articles worthy of reading. Our evaluation also provides quantitative and qualitative insights into the curation of links, from the perspective of users and professional editors.
  11. Arapakis, I.; Lalmas, M.; Cambazoglu, B.B.; MarcosM.-C.; Jose, J.M.: User engagement in online news : under the scope of sentiment, interest, affect, and gaze (2014) 0.01
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    Abstract
    Online content providers, such as news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, through an analysis of a news article collection obtained from Yahoo News US, we demonstrate that news articles exhibit considerable variation in terms of the sentimentality and polarity of their content, depending on factors such as news provider and genre. Moreover, through a laboratory study, we observe the effect of sentimentality and polarity of news and comments on a set of subjective and objective measures of engagement. In particular, we show that attention, affect, and gaze differ across news of varying interestingness. As part of our study, we also explore methods that exploit the sentiments expressed in user comments to reorder the lists of comments displayed in news pages. Our results indicate that user engagement can be anticipated predicted if we account for the sentimentality and polarity of the content as well as other factors that drive attention and inspire human curiosity.
  12. Goyal, N.; Bron, M.; Lalmas, M.; Haines, A.; Cramer, H.: Designing for mobile experience beyond the native ad click : exploring landing page presentation style and media usage (2018) 0.01
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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.
  13. Nikolov, D.; Lalmas, M.; Flammini, A.; Menczer, F.: Quantifying biases in online information exposure (2019) 0.01
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    Abstract
    Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."
  14. Ruthven, I.; Lalmas, M.; Rijsbergen, K. van: Combining and selecting characteristics of information use (2002) 0.01
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    Abstract
    Ruthven, Lalmas, and van Rijsbergen use traditional term importance measures like inverse document frequency, noise, based upon in-document frequency, and term frequency supplemented by theme value which is calculated from differences of expected positions of words in a text from their actual positions, on the assumption that even distribution indicates term association with a main topic, and context, which is based on a query term's distance from the nearest other query term relative to the average expected distribution of all query terms in the document. They then define document characteristics like specificity, the sum of all idf values in a document over the total terms in the document, or document complexity, measured by the documents average idf value; and information to noise ratio, info-noise, tokens after stopping and stemming over tokens before these processes, measuring the ratio of useful and non-useful information in a document. Retrieval tests are then carried out using each characteristic, combinations of the characteristics, and relevance feedback to determine the correct combination of characteristics. A file ranks independently of query terms by both specificity and info-noise, but if presence of a query term is required unique rankings are generated. Tested on five standard collections the traditional characteristics out preformed the new characteristics, which did, however, out preform random retrieval. All possible combinations of characteristics were also tested both with and without a set of scaling weights applied. All characteristics can benefit by combination with another characteristic or set of characteristics and performance as a single characteristic is a good indicator of performance in combination. Larger combinations tended to be more effective than smaller ones and weighting increased precision measures of middle ranking combinations but decreased the ranking of poorer combinations. The best combinations vary for each collection, and in some collections with the addition of weighting. Finally, with all documents ranked by the all characteristics combination, they take the top 30 documents and calculate the characteristic scores for each term in both the relevant and the non-relevant sets. Then taking for each query term the characteristics whose average was higher for relevant than non-relevant documents the documents are re-ranked. The relevance feedback method of selecting characteristics can select a good set of characteristics for query terms.
  15. Blanke, T.; Lalmas, M.; Huibers, T.: ¬A framework for the theoretical evaluation of XML retrieval (2012) 0.01
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    Abstract
    We present a theoretical framework to evaluate XML retrieval. XML retrieval deals with retrieving those document components-the XML elements-that specifically answer a query. In this article, theoretical evaluation is concerned with the formal representation of qualitative properties of retrieval models. It complements experimental methods by showing the properties of the underlying reasoning assumptions that decide when a document is about a query. We define a theoretical methodology based on the idea of "aboutness" and apply it to current XML retrieval models. This allows comparing and analyzing the reasoning behavior of XML retrieval models experimented within the INEX evaluation campaigns. For each model we derive functional and qualitative properties that qualify its formal behavior. We then use these properties to explain experimental results obtained with some of the XML retrieval models.
  16. Lalmas, M.: XML retrieval (2009) 0.00
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    Series
    Synthesis lectures on information concepts, retrieval & services; 7