Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System

Ricci, Francesco; Nguyen, Quoc‐Hung

doi:10.1109/mis.2007.43

Cited by 156 publications

(111 citation statements)

References 4 publications

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“…Roberts et al (2008) describe the implementation of a mobile recommender system for leisure activities, named Magitti, which was built for commercial deployment under stringent scalability requirements. Ricci & Nguyen (2007) exploit the recommendation by proposal and critiquing techniques in the MobyRek system, that has been designed to run on a mobile phone with limited user input. It searches functionality, lets the user to formulate both must and wish conditions, and returns a ranked product list.…”

Section: Mobile Recommender Systemsmentioning

confidence: 99%

“…Therefore, the mechanisms using this approach are able to effectively deal with traditional security systems limitations that are designed for static environments. Nevertheless, most of the research regarding the development of context-aware authentication systems is limited or vague (Ricci & Nguyen, 2007). Usually, these systems only consider traditional aspects, e.g.…”

Section: Pervasive Security and Recommender Systemsmentioning

confidence: 99%

See 1 more Smart Citation

CARS-AD Project: Context-Aware Recommender System for Authentication Decision in Pervasive and Mobile Environments

Lima¹,

Rocha²,

Augustin³

et al. 2012

Advances and Applications in Mobile Computing

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Section: Mobile Recommender Systemsmentioning

confidence: 99%

Section: Pervasive Security and Recommender Systemsmentioning

confidence: 99%

CARS-AD Project: Context-Aware Recommender System for Authentication Decision in Pervasive and Mobile Environments

Lima¹,

Rocha²,

Augustin³

et al. 2012

Advances and Applications in Mobile Computing

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“…The usage context is usually characterized by the hour of the day, the day of the week and the location of the user, whereas the user's preferences are typically expressed with versatile profiles. In a more elaborative way, the user preferences can be further categorized into session-specific and long-term preferences [7].…”

Section: Introductionmentioning

confidence: 99%

“…[1] [7]) already exist that guide users to restaurants and bars according to the criteria such as the menu and the type of the restaurant or bar, the average price level, the distance from the user's current location, and the accepted methods of payment. However, especially as for the bars and nightclubs, the recommendations could also be given based on the match between the user's personal music preferences [8] and the music style of the entertainment premises.…”

Section: Introductionmentioning

confidence: 99%

User Experience in Added Value Location-Based Mobile Music Service

Koskela

Järvinen

Liu

et al. 2010

2010 IEEE International Conference on Web Services

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Abstract-In this paper, the user experience in added value location-based mobile music service called the City Night Life (CNL) is evaluated and analyzed. The CNL service provides recommendations about entertainment premises in the proximity by matching their music styles with the user's personal music preferences. Entertainment premises are shown on a map interface and are marked with colors according to their match. The paper presents the components of the CNL service, the music profile, matching algorithm and the map-based user interface in a web browser. The user evaluation with 53 test users assessed the feasibility and business potential of the CNL service. Data was collected with both quantitative questionnaires and short interviews. The results indicate that the CNL service will be most useful in relatively large, unfamiliar cities. Furthermore, the matching algorithm was perceived to perform quite well.

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“…In a previous paper [15], we presented our critique-based recommendation approach and its implementation in MobyRek, a mobile case-based RS that helps mobile users find their desired travel products (restaurants). In that paper, we also presented a live-user evaluation of MobyRek, and the experimental results showed that our recommendation methodology is effective in supporting mobile users in making product selection decisions.…”

Section: Introductionmentioning

confidence: 99%

Conversational Case-Based Recommendations Exploiting a Structured Case Model

Nguyen

Ricci

Lecture Notes in Computer Science

Self Cite

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Abstract. There are case-based recommender systems that generate personalized recommendations for users exploiting the knowledge contained in past recommendation cases. These systems assume that the quality of a new recommendation depends on the quality of the recorded recommendation cases. In this paper, we present a case model exploited in a mobile critique-based recommender system that generates recommendations using the knowledge contained in previous recommendation cases. The proposed case model is capable of modeling evolving (conversational) recommendation sessions, capturing the recommendation context, supporting critique-based user-system conversations, and integrating both ephemeral and stable user preferences. In this paper, we evaluate the proposed case model through replaying real recommendation cases recorded in a previous live-user evaluation. We measure the impact of the various components of the case model on the system's recommendation performance. The experimental results show that the case components that model the user's contextual information, default preferences, and initial preferences, are the most important for mobile context-dependent recommendation.

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Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System

Cited by 156 publications

References 4 publications

CARS-AD Project: Context-Aware Recommender System for Authentication Decision in Pervasive and Mobile Environments

CARS-AD Project: Context-Aware Recommender System for Authentication Decision in Pervasive and Mobile Environments

User Experience in Added Value Location-Based Mobile Music Service

Conversational Case-Based Recommendations Exploiting a Structured Case Model

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