PersonisJ: Mobile, Client-Side User Modelling

Gerber, Simon; Fry, Michael; Kay, Judy; Kummerfeld, Bob; Pink, Glen; Wasinger, Rainer

doi:10.1007/978-3-642-13470-8_12

Cited by 31 publications

(19 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jane has used Menu Mentor before and has previously provided information on her likes, dislikes, and allergies. These are now stored in her client-side PersonisJ user model [6]. Her user model also contains individual meal ratings, and the weightings for specific ingredients, based on Jane's previous ratings of other restaurant meals.…”

Section: Scenario Walkthroughmentioning

confidence: 99%

“…The Menu Mentor application makes use of the PersonisJ mobile client-side user-modelling framework [6]. This runs on Android devices and provides its users with the ability to securely store and share user information between applications on the mobile device as well as to synchronise such information with a user's cloud-based user model (Figure 3, right).…”

Section: Restaurant Data Repository Andmentioning

confidence: 99%

See 1 more Smart Citation

Scrutable User Models and Personalised Item Recommendation in Mobile Lifestyle Applications

Wasinger

Wallbank

Pizzato

et al. 2013

User Modeling, Adaptation, and Personalization

Self Cite

View full text Add to dashboard Cite

Abstract. This paper presents our work on supporting scrutable user models for use in mobile applications that provide personalised item recommendations. In particular, we describe a mobile lifestyle application in the fine-dining domain, designed to recommend meals at a particular restaurant based on a person's user model. The contributions of this work are three-fold. First is the mobile application and its personalisation engine for item recommendation using a content and critique-based hybrid recommender. Second, we illustrate the control and scrutability that a user has in configuring their user model and browsing a content list. Thirdly, this is validated in a user experiment that illustrates how new digital features may revolutionise the way that paper-based systems (like restaurant menus) currently work. Although this work is based on restaurant menu recommendations, its approach to scrutability and mobile client-side personalisation carry across to a broad class of commercial applications.

show abstract

Section: Scenario Walkthroughmentioning

confidence: 99%

Section: Restaurant Data Repository Andmentioning

confidence: 99%

Scrutable User Models and Personalised Item Recommendation in Mobile Lifestyle Applications

Wasinger

Wallbank

Pizzato

et al. 2013

User Modeling, Adaptation, and Personalization

Self Cite

View full text Add to dashboard Cite

show abstract

“…in History or Science, in a certain kind of music, in nature, in children stuff, in sports, etc). SpeciÀcally, people go to museums during their spare time purposefully to learn about a speciÀc subject, which makes them propitious places to go beyond the individual use of mobile devices promoted by the many previous pieces of work that provided personalised itineraries within the buildings, continuity of experiences from one visit to another, etc [8], [9], [10], [11], [12], [13], [14].…”

Section: Sporangium Features For Museum-like Venuesmentioning

confidence: 99%

Leveraging Short-Lived Social Networks in Museums to Engage People in History Learning

López‐Nores

Blanco‐Fernández

Gil‐Solla

et al. 2013

2013 8th International Workshop on Semantic and Social Media Adaptation and Personalization

View full text Add to dashboard Cite

The interactions enabled by the popular sites of the Web 2.0 are largely conÀned to the virtual world of the Internet, thus failing to engage people in relevant interactions with people, contents or resources in their physical environment. In this paper, we motivate the potential of automatically establishing sporadic social networks among people (acquaintances or strangers) who happen to be physically close to one another at a certain moment. We present the design of one platform intended to provide solutions from the lowest level of establishing ad-hoc connections among nearby mobile devices, up to the highest level of automatically identifying the most relevant pieces of information to deliver at any time. One speciÀc scenario is discussed for museum environments, considering the possibility of engaging groups of people into immersive collective experiences to learn about major historical events.

show abstract

“…The key idea behind client-based personalization is that users' data are stored at the client side (e.g., users' computers or mobile phones) and subsequent personalization processes also take place at the client side (Cassel et al 2001;Ceri et al 2004;Coroama and Langheinrich 2006;Mulligan and Schwartz 2000;Gerber et al 2010). Since data collection and processing occur at the client side rather than the server side, users may perceive more control over their data and perceive less privacy risk.…”

Section: Client-side Personalizationmentioning

confidence: 99%

Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

Toch

Wang

Cranor

2012

User Model User-Adap Inter

163

132

View full text Add to dashboard Cite

Personalization technologies offer powerful tools for enhancing the user experience in a wide variety of systems, but at the same time raise new privacy concerns. For example, systems that personalize advertisements according to the physical location of the user or according to the user's friends' search history, introduce new privacy risks that may discourage wide adoption of personalization technologies. This article analyzes the privacy risks associated with several current and prominent personalization trends, namely social-based personalization, behavioral profiling, and location-based personalization. We survey user attitudes towards privacy and personalization, as well as technologies that can help reduce privacy risks. We conclude with a discussion that frames risks and technical solutions in the intersection between personalization and privacy, as well as areas for further investigation. This frameworks can help designers and researchers to contextualize privacy challenges of solutions when designing personalization systems.

show abstract

PersonisJ: Mobile, Client-Side User Modelling

Cited by 31 publications

References 13 publications

Scrutable User Models and Personalised Item Recommendation in Mobile Lifestyle Applications

Scrutable User Models and Personalised Item Recommendation in Mobile Lifestyle Applications

Leveraging Short-Lived Social Networks in Museums to Engage People in History Learning

Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

Contact Info

Product

Resources

About