Search (112 results, page 1 of 6)

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Abstract

This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users' privacy protection, i.e., it is required to comprehensively improve the security of users' preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

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Abstract

Purpose Continuous knowledge sharing by active users, who are highly active in answering questions, is crucial to the sustenance of social question-and-answer (Q&A) sites. The purpose of this paper is to examine such knowledge sharing considering reason-based elaborate decision and habit-based automated cognitive processes. Design/methodology/approach To verify the research hypotheses, survey data on subjective intentions and web-crawled data on objective behavior are utilized. The sample size is 337 with the response rate of 27.2 percent. Negative binomial and hierarchical linear regressions are used given the skewed distribution of the dependent variable (i.e. the number of answers). Findings Both elaborate decision (linking satisfaction, intentions and continuance behavior) and automated cognitive processes (linking past and continuance behavior) are significant and substitutable. Research limitations/implications By measuring both subjective intentions and objective behavior, it verifies a detailed mechanism linking continuance intentions, past behavior and continuous knowledge sharing. The significant influence of automated cognitive processes implies that online knowledge sharing is habitual for active users. Practical implications Understanding that online knowledge sharing is habitual is imperative to maintaining continuous knowledge sharing by active users. Knowledge sharing trends should be monitored to check if the frequency of sharing decreases. Social Q&A sites should intervene to restore knowledge sharing behavior through personalized incentives. Originality/value This is the first study utilizing both subjective intentions and objective behavior data in the context of online knowledge sharing. It also introduces habit-based automated cognitive processes to this context. This approach extends the current understanding of continuous online knowledge sharing behavior.

Date

20. 1.2015 18:30:22

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Abstract

With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

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Abstract

Plurality of beliefs and theories in different knowledge domains calls for modelling multi-viewpoint ontologies and knowledge organization systems (KOS). A generic theoretical approach recently proposed for heterogeneity representation in KOS was linking each ontological statement to a specific validity scope to determine a set of conditions under which the statement is valid. However, the practical applicability of this approach has yet to be empirically assessed. In addition, there is still a need to investigate the types of inconsistencies that might arise in multi-viewpoint ontologies as well as their possible causes. This study proposes a new framework for systematic analysis and classification of inconsistencies in multi-viewpoint ontologies. The framework is based on eight generic logical structures of ontological statements. To test the validity of the proposed framework, two ontologies from different knowledge domains were examined. We found that only three of the eight structures led to inconsistencies in both ontologies, while the other two structures were always present in logically consistent statements. The study has practical implications for building diversified and personalized knowledge systems.

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Abstract

Knowing what information a user wants is a paramount challenge to information science and technology. Implicit feedback is key to solving this challenge, as it allows information systems to learn about a user's needs and preferences. The available feedback, however, tends to be limited and its interpretation shows to be difficult. To tackle this challenge, we present a user study that explores whether tracking the eyes can unpack part of the complexity inherent to relevance and relevance decisions. The eye behavior of 30 participants reading 18 news articles was compared with their subjectively appraised comprehensibility and interest at a discourse level. Using linear regression models, the eye-tracking signal explained 49.93% (comprehensibility) and 30.41% (interest) of variance (p < .001). We conclude that eye behavior provides implicit feedback beyond accuracy that enables new forms of adaptation and interaction support for personalized information systems.

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Abstract

Social media connected the world--and gave rise to fake news and increasing polarization. Now a leading researcher at MIT draws on 20 years of research to show how these trends threaten our political, economic, and emotional health in this eye-opening exploration of the dark side of technological progress. Today we have the ability, unprecedented in human history, to amplify our interactions with each other through social media. It is paramount, MIT social media expert Sinan Aral says, that we recognize the outsized impact social media has on our culture, our democracy, and our lives in order to steer today's social technology toward good, while avoiding the ways it can pull us apart. Otherwise, we could fall victim to what Aral calls "The Hype Machine." As a senior researcher of the longest-running study of fake news ever conducted, Aral found that lies spread online farther and faster than the truth--a harrowing conclusion that was featured on the cover of Science magazine. Among the questions Aral explores following twenty years of field research: Did Russian interference change the 2016 election? And how is it affecting the vote in 2020? Why does fake news travel faster than the truth online? How do social ratings and automated sharing determine which products succeed and fail? How does social media affect our kids? First, Aral links alarming data and statistics to three accelerating social media shifts: hyper-socialization, personalized mass persuasion, and the tyranny of trends. Next, he grapples with the consequences of the Hype Machine for elections, businesses, dating, and health. Finally, he maps out strategies for navigating the Hype Machine, offering his singular guidance for managing social media to fulfill its promise going forward. Rarely has a book so directly wrestled with the secret forces that drive the news cycle every day"

Content

Inhalt: Pandemics, Promise, and Peril -- The New Social Age -- The End of Reality -- The Hype Machine -- Your Brain on Social Media -- A Network's Gravity is Proportional to Its Mass -- Personalized Mass Persuasion -- Hypersocialization -- Strategies for a Hypersocialized World -- The Attention Economy and the Tyranny of Trends -- The Wisdom and Madness of Crowds -- Social Media's Promise Is Also Its Peril -- Building a Better Hype Machine.

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Abstract

A mobile device-enabled experience sampling study was conducted in which 44 participants answered questions about their music experiences 5 times a day for 2 weeks. Data regarding 4 aspects of their music-related psychological traits-"music involvement," "musical identity," "preference diversity," and "preference openness"-were also collected through a background questionnaire. A classification of music access modes was proposed based on the circumstances that lead to a music listening experience. A mixed regression analysis revealed several significant interaction effects between psychological traits and the mode of music access on music enjoyment. Foremost among these was a positive interaction effect between preference openness and the playing of a known track triggered by musical cues, and that between preference diversity and exposure to new music played by others. Individuals with a strong musical identity tended to enjoy music played of their own volition without any apparent triggers. Furthermore, a multimodal logistic regression analysis also revealed the relationships between these psychological traits and the likelihood of different music access modes. Preference diversity significantly increased the likelihood of music listening triggered by need arousal. The results support the proposition that users' music-related psychological traits should be considered in personalized recommendation strategies.

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Abstract

Information service providers often require evidence from multiple, heterogeneous information sources to better characterize users and offer personalized service. In many cases, statistic information (for example, users' profiles) and sequentially dynamic information (for example, logs of interaction with information systems) are two prominent sources that can be combined to achieve optimized results. Previous attempts in combining these two sources mainly exploited models designed for either static or sequential information, but not both. This study aims to fill the gap by proposing a novel joint neural network model that can naturally fit both static and sequential user data. To evaluate the effectiveness of the proposed method, this study uses the problem of at-risk student prediction as an example where both static data (personal profiles) and sequential data (event logs) are involved. A thorough evaluation was conducted on an open data set, with comparisons to a range of existing approaches including both static and sequential models. The results reveal superb performances of the proposed method. Implications of the findings on further research and applications of joint models are discussed.

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Abstract

CommunityRx is a theory-driven, information technology-based intervention, developed with and in a predominantly African American/Black community, that provides patients with personalized information (a "HealtheRx") about self-management and social care resources in their community. We described patient and clinician information sharing after exposure to the intervention during a clinical trial. Survey data from 145 patients (ages 45-74) and 121 clinicians were analyzed. Of patients who shared information at least once (49%), 47% reported sharing =3 times (range 1-14). Patient sharers were in poorer physical health (mean PCS 37.6 vs. 40.8, p = .05) than nonsharers and more likely to report going to a resource on their HealtheRx (79 vs. 41%, p = .05). Most patient sharers provided others a look at or copy of their HealtheRx, keeping the original. Patients used the HealtheRx to promote credibility of the information and communicate that resources were disease-specific and local. Half of clinicians shared HealtheRx resource information with peers; sharers were 3 times more likely than nonsharers to feel they were well-informed about resources to address social needs (55 vs. 18%, p < .01). Information sharing by clinicians and patients is an understudied mechanism that could amplify the effects of a growing class of community resource referral information technologies.

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Abstract

Social interactions through online social media have become a daily routine of many, and the number of those whose real world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact-the effect of real-world activities on online actions-has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi-experimental design study that examined the presence of causal relations between real-world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check-ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi-experimental design, to minimize the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarized as follows: (a) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (b) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalized recommendation mechanism to help people better manage their online emotional expressions.

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Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

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Content

"Die Englischsprachige Wikipedia verfügt jetzt über mehr als 6 Millionen Artikel. An zweiter Stelle kommt die deutschsprachige Wikipedia mit 2.3 Millionen Artikeln, an dritter Stelle steht die französischsprachige Wikipedia mit 2.1 Millionen Artikeln (via Researchbuzz: Firehose <https://rbfirehose.com/2020/01/24/techcrunch-wikipedia-now-has-more-than-6-million-articles-in-english/> und Techcrunch <https://techcrunch.com/2020/01/23/wikipedia-english-six-million-articles/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&guccounter=1&guce_referrer=aHR0cHM6Ly9yYmZpcmVob3NlLmNvbS8yMDIwLzAxLzI0L3RlY2hjcnVuY2gtd2lraXBlZGlhLW5vdy1oYXMtbW9yZS10aGFuLTYtbWlsbGlvbi1hcnRpY2xlcy1pbi1lbmdsaXNoLw&guce_referrer_sig=AQAAAK0zHfjdDZ_spFZBF_z-zDjtL5iWvuKDumFTzm4HvQzkUfE2pLXQzGS6FGB_y-VISdMEsUSvkNsg2U_NWQ4lwWSvOo3jvXo1I3GtgHpP8exukVxYAnn5mJspqX50VHIWFADHhs5AerkRn3hMRtf_R3F1qmEbo8EROZXp328HMC-o>). 250120 via digithek ch = #fineBlog s.a.: Angesichts der Veröffentlichung des 6-millionsten Artikels vergangene Woche in der englischsprachigen Wikipedia hat die Community-Zeitungsseite "Wikipedia Signpost" ein Moratorium bei der Veröffentlichung von Unternehmensartikeln gefordert. Das sei kein Vorwurf gegen die Wikimedia Foundation, aber die derzeitigen Maßnahmen, um die Enzyklopädie gegen missbräuchliches undeklariertes Paid Editing zu schützen, funktionierten ganz klar nicht. *"Da die ehrenamtlichen Autoren derzeit von Werbung in Gestalt von Wikipedia-Artikeln überwältigt werden, und da die WMF nicht in der Lage zu sein scheint, dem irgendetwas entgegenzusetzen, wäre der einzige gangbare Weg für die Autoren, fürs erste die Neuanlage von Artikeln über Unternehmen zu untersagen"*, schreibt der Benutzer Smallbones in seinem Editorial <https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2020-01-27/From_the_editor> zur heutigen Ausgabe."

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Content

Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.

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Date

27. 1.2023 16:22:55

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Date

2. 3.2020 14:08:22

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Source

Spektrum der Wissenschaft. 2023, H.10, S.22-24

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Date

17. 1.2020 22:15:11

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Date

4. 5.2020 17:22:40

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Date

4. 5.2020 17:22:28

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Date

22. 6.2020 19:11:24