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IEEE PERVASIVE COMPUTING 2021年第1期20卷 4-6页

作者： Langheinrich, Marc Univ Svizzera Italiana Fac Informat CH-6900 Lugano Switzerland

With the pandemic going into its second year, many are questioning its long-term impact, not only on things like health and economy, but also on more minute details of our lives. Pervasive computing has a long tradition of seeking to change the "details" of our lives-does our field already have answers on how to measure such long-term implications?

关键词： advertising advertising data processing data Privacy Internet Of Things Web Sites Online Advertisement Company Personalized Advertisements Cookies Code Name F Lo C

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Licenseyourvoice

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Engineering and Technology 2021年第8期16卷 79-84页

作者： Doyle, Siobhan

An AI company in the US has launched a hyper-realistic Voice as-a-Service (VaaS) solution that enables users to 'create, manage, license and monetise’ synthetic voices. SYNTHETIC voices have increasingly become ubiquitous in our everyday lives, from the news we hear on our smart speakers to giving us directions to a desired destination.

关键词： security best-in-class voice engines cloud computing neural networks AI model fingerprinting broadcasting ethical use celebrities public figures Open Voice Notebook secure cloud environment speech synthesis AI operating system operating systems (computers) neural nets Communications computing security of data Marketing computing machine learning Internet software adjustable deepfake voice clones advertising speech processing Voice-as-a-Service Marvel.AI platform virtual chatting broadcasting networks text-to-speech Veritone synthetic voice VAAS Speech recognition and synthesis audio sample analysis advertising data processing audio signal processing audio file token embedding Speech processing techniques speech-to-speech aiWARE talent voice brand learning (artificial intelligence) ad regionalisation Natural language processing auto-dubbing

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Multimedia data Privacy Against Machines

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IEEE MULTIMEDIA 2020年第2期27卷 5-7页

作者： Kankanhalli, Mohan Natl Univ Singapore Singapore Singapore

With the explosion of multimedia data, machine learning applications are proliferating. Privacy concerns have recently resurfaced, due to a few prominent incidents of user data leakage, which divulged sensitive information to other people. Multimedia data platform providers-social networks, their affiliates or governments with access to users' content-use algorithms to profile users by extracting or inferring demographic information, personality traits, relationships, opinions, and beliefs. These results, in turn, feed algorithms for targeted advertising but also for personalized content recommendation to maximize user engagement. While this is ostensibly for users' benefit, they have serious privacy implications. We highlight this new problem of privacy protection against machines in contrast to the traditional problem of privacy protection against humans. We briefly touch upon our initial solution, a human-sensitivity-aware image perturbation model, which is able to modify the computational classification results of sensitive attributes while preserving the remaining attributes. We then point to many exciting open problems in this new area.

关键词： advertising data processing data Protection Learning Artificial Intelligence Multimedia Computing Recommender Systems Social Networking Online Multimedia data Privacy Machine Learning Applications Sensitive Information Demographic Information Personality Traits Feed Algorithms Targeted advertising Personalized Content Recommendation User Engagement Serious Privacy Implications Privacy Protection Human Sensitivity Aware Image Perturbation Model Computational Classification Results Sensitive Attributes Multimedia data Platform Providers Social Networks data Privacy Streaming Media Privacy Social Networking Online Machine Learning Algorithms Machine Learning Government

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Online marketing mode of homestay based on collaborative filtering algorithm

Online marketing mode of homestay based on collaborative fil...

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The International Conference on Forthcoming Networks and Sustainability (FoNeS 2022)

作者： H. Yu Nanning University Nanning Guangxi People's Republic of China

With the popularity of the Internet, network marketing has developed rapidly. In order to help B & Bs find more potential users, increase the conversion rate of advertisements, and improve customer income, a series of products are provided on the advertising platforms of various Internet companies, so how to effectively promote products to customers is particularly important. The purpose of this paper is to study the online marketing mode of homestay based on collaborative filtering algorithm. This paper mainly introduces the rise of the network marketing model and the advantages of the network active marketing model. Based on the traditional collaborative filtering calculation, the algorithm is improved, and the recommendation system is improved, so that the network marketing of the homestay will be richer and faster. Experiments show that there are 40.74 million new netizens in a single year, and the penetration rate of the Internet is increasing day by day. Therefore, we must pay attention to the importance of homestays in online marketing.

关键词： advertising data processing collaborative filtering Internet recommender systems

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Puncturing Pernicious Project Pufferies

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COMPUTER 2018年第5期51卷 78-83页

作者： Charette, Robert N. ITABHI Corp Spotsylvania Courthouse VA 22551 USA

More disgruntled customers are filing lawsuits against vendors when their IT projects go bad, typically alleging the vendors didn't live up to their promises. Courts, however, routinely dismiss these lawsuits, cit... 详细信息

关键词： advertising data processing DP industry legislation

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Utilizing Verbal Intent in Semantic Contextual advertising

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IEEE INTELLIGENT SYSTEMS 2017年第3期32卷 7-13页

作者： Ryu, Woo-Jong Lee, Jung-Hyun Lee, SangKeun Korea Univ Coll Informat & Commun Seoul South Korea Korea Univ Dept Comp Sci & Engn Seoul South Korea

Existing semantic approaches to contextual advertising effectively match relevant ads to webpages in terms of topical intent. The authors observe, however, that there"s still much room for improvement. In this article, they seek to utilize the verbal intent, which complements topical intent, in semantic contextual advertising. Verbal intent describes what a user wants to do, that is, the action perspective, with topical intent. The authors propose a methodology that effectively identifies verbal intent in webpages and ads, and then incorporates the verbal intent into an ad-ranking framework. The results of performance evaluation on real-world datasets clearly show the efficacy of (verb, topic) associations. Compared with state-of-the-art techniques, the proposed methodology exhibits a significant improvement of precision at a high level of recall in ad ranking, as well as a precision improvement of 35 percent on average in verb identification.

关键词： advertising Semantics Context awareness Taxonomy Information retrieval Computational modeling INSPEC: Controlled Indexing advertising data processing search engines semantic Web INSPEC: Non-Controlled Indexing verbal intent semantic contextual advertising Webpages adranking Author Keywords content analysis and indexing information search and retrieval text mining intelligent systems artificial intelligence

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Impact of trust and perceived content of advertisement on intention to accept mobile advertisement

Impact of trust and perceived content of advertisement on in...

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International Conference on Digital Information processing, data Mining, and Wireless Communications (DIPDMWC)

作者： Seyed Mohammadbagher Jafari Hadi Taghavi Mozhgan Yeganeh Daryakenari Shirin Shahbazi Faculty of Management and Accounting University of Tehran Iran

ISBN: (纸本)9781467393805

A very new issue in current age of marketing is mobile advertisement to which there is a little recognition on its effectiveness. Ignoring users' interests and needs as well as sending huge volume of mobile advertisements leads into wasting advertisement investment. Firms should utilize different strategies to design and send mobile advertisement. The developed model for this study considers affecting factors on intention to accept mobile advertisements by consumers. To test the research model, the data from a sample of 436 mobile users was gathered by judgment sampling method via questionnaire. data analysis was conducted by structural equation modeling (SEM) in AMOS software. Results indicate that user's trust, entertainment, informativeness and ground determine 54% of changes in attitude toward accepting mobile advertisement. Additionally, trust in the third party has remarkable indirect impact on intention to accept this kind of advertisements.

关键词： statistical analysis advertising data processing data analysis mobile commerce social aspects of automation

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Using Statistical data for Context Sensitive Pervasive advertising 4

Using Statistical Data for Context Sensitive Pervasive Adver...

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4th IEEE International Conference on Consumer Electronics Berlin (ICCE-Berlin)

作者： Bublitz, Frederico M. Rosner, Marco Almeida, Hyggo O. de Lima, Aislan M. Batista, Thiago S. State Univ Paraiba UEPB Ctr Strateg Technol Hlth NUTES Campina Grande Paraiba Brazil Fed Univ Campina Grande UFCG Embedded Syst & Pervas Comp Lab Campina Grande Paraiba Brazil

ISBN: (纸本)9781479961658

smartphones are available anytime and anywhere, enabling applications to determine the user location. In a previous work, we demonstrate how we ca use this information for inferring the age, gender, and social class based on the places visited by the user. In this work, we investigate the hypothesis that it is possible to deliver relevant advertisements based on statistical data, associated with basic customer information inferred from location. To validate our hypothesis, we conducted an experiment comparing the relevance of the advertisements of our approach with collaborative filtering.

关键词： advertising data processing data handling mobile computing advertisements collaborative filtering context sensitive pervasive advertising customer information smartphones statistical data advertising Collaboration Context Filtering Media Mobile communication Pervasive computing Statistical data Customer information advertising data processing Advertisements Smartphone collaborative filtering advertising as Topic data processing Ubiquitous computing mobile communication Representation of data Mobile Computing Filtration

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PIPeR: Impact of Power-Awareness on Social-Based Opportunistic advertising

PIPeR: Impact of Power-Awareness on Social-Based Opportunist...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Al Ayyat, Soumaia Aly, Sherif G. Harras, Khaled A. Amer Univ Cairo Dept Comp Sci & Engn Cairo Egypt Carnegie Mellon Univ Qatar Sch Comp Sci Doha Qatar

ISBN: (纸本)9781479930838

Interest and social-awareness can be valuable determinants in decisions related to content delivery in mobile environments. Under certain conditions, we can deliver content with less cost and better delivery ratios, while only involving users that are interested in the type of content being delivered. However, the depletion of valuable power resources poses a deterrent to node participation in such interest-aware forwarding systems. No significant research contribution has been identified to collectively maximize the benefits of social, interest, and power awareness. In this work, we propose a new algorithm called PIPeR which integrates power awareness with an interest and socially aware forwarding algorithm called IPeR. Through simulations, we present and evaluate four modes of PIPeR. The results show that PIPeR is more fair and preserves at least 22% of the power IPeR consumes with less delay, while relying significantly on interested forwarders and with comparable cost to maintain similar delivery ratios.

关键词： advertising data processing mobile computing PIPeR content delivery interest awareness interest-aware forwarding systems mobile environments power awareness social awareness social-awareness social-based opportunistic advertising socially aware forwarding algorithm Batteries Communities Delays Mobile nodes Routing protocols Social Skills advertising data processing Mobile Environment Piper Content distribution Routing protocols Mobile Computing Batteries Energy sources Mobile nodes delay

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Personalising Mobile advertising Based on Users' Installed Apps 14

Personalising Mobile Advertising Based on Users' Installed A...

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14th IEEE International Conference on data Mining (IEEE ICDM)

作者： Reps, Jenna Aickelin, Uwe Garibaldi, Jonathan Damski, Chris Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England Univ Nottingham Adv Data Anal Ctr Nottingham NG8 1BB England Opera Mediaworks London SE1 7NX England

ISBN: (纸本)9781479942749

Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whether the application of unsupervised learning and association rule mining could be used to enable personalised targeting of mobile adverts with the aim of increasing the interaction rate. Over May and June 2014 we recorded advert interactions such as tapping the advert or watching the whole advert video along with the set of apps a user has installed at the time of the interaction. Based on the apps that the users have installed we applied k-means clustering to profile the users into one of ten classes. Due to the large number of apps considered we implemented dimension reduction to reduced the app feature space by mapping the apps to their iTunes category and clustered users based on the percentage of their apps that correspond to each iTunes app category. The clustering was externally validated by investigating differences between the way the ten profiles interact with the various adverts genres (lifestyle, finance and entertainment adverts). In addition association rule mining was performed to find whether the time of the day that the advert is served and the number of apps a user has installed makes certain profiles more likely to interact with the advert genres. The results showed there were clear differences in the way the profiles interact with the different advert genres and the results of this paper suggest that mobile advert targeting would improve the frequency that users interact with an advert.

关键词： advertising data processing data mining human computer interaction mobile commerce pattern clustering unsupervised learning advert interactions advert tapping advert video watching app feature space apps mapping association rule mining dimension reduction iTunes app category interaction rate k-means clustering mobile advertising personalisation mobile adverts user installed apps user interaction user profile Association rules Entertainment industry Indexes Media Mobile communication Mobile handsets Vectors Unsupervised learning Association rules advertising data processing Mobile phones user profiling computer human interaction Mobile commerce Consumers user-computer interaction mobile communication Cluster Analysis data mining

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