Activity-based serendipitous recommendations with the Magitti mobile leisure guide

Bellotti, Victoria; Begole, Bo; H., Ed; Ducheneaut, Nicolas; Fang, Jingyang; Isaacs, Ellen; King, Tracy Holloway; Newman, Mark; Partridge, Kurt; Price, Bob; Rasmussen, Paul; Roberts, Margaret; Schiano, Diane J.; Walendowski, Alan

doi:10.1145/1357054.1357237

Cited by 138 publications

(94 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Location-based information services are typically provided as recommendations [3] or as contextualized search results [18]. Several surveys show that restaurants and stores are the most popular locations that users search for, followed by local attractions and locations associated with leisure time [3,18].…”

Section: Location-based Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Predicting User Locations and Trajectories

Herder

Siehndel

Kawase

2014

User Modeling, Adaptation, and Personalization

View full text Add to dashboard Cite

Abstract. Location-based services usually recommend new locations based on the user's current location or a given destination. However, human mobility involves to a large extent routine behavior and visits to already visited locations. In this paper, we show how daily and weekly routines can be modeled with basic prediction techniques. We compare the methods based on their performance, entropy and correlation measures. Further, we discuss how location prediction for everyday activities can be used for personalization techniques, such as timely or delayed recommendations.

show abstract

Section: Location-based Servicesmentioning

confidence: 99%

“…Several surveys show that restaurants and stores are the most popular locations that users search for, followed by local attractions and locations associated with leisure time [3,18]. As noted before, these services usually provide suggestions for new locations, based on the user's preferences and current location.…”

Section: Location-based Servicesmentioning

confidence: 99%

Predicting User Locations and Trajectories

Herder

Siehndel

Kawase

2014

User Modeling, Adaptation, and Personalization

View full text Add to dashboard Cite

show abstract

“…Moreover, technology support of leisure activities are relevant, such as cultural visiting (Brown et al, 2005;Bellotti et al, 2008), spectating at outdoor sports events (Jacucci et al, 2007;Salovaara et al, 2006), location-based games (Bell et al, 2006), and learning (Benford et al, 2005).…”

Section: Notifications In Collaborative Systemsmentioning

confidence: 99%

Understanding Mobile Notification Management in Collocated Groups

Fischer

Reeves

Moran

et al. 2013

ECSCW 2013: Proceedings of the 13th European Conference on Computer Supported Cooperative Work, 21-25 September 2013, Paphos, C

View full text Add to dashboard Cite

Abstract. We present an observational study of how notifications are handled by collocated groups, in the context of a collaborative mobile photo-taking exercise. Interaction analysis of video recordings is used to uncover the methodical ways in which participants manage notifications, establishing and sustaining co-oriented interaction to coordinate action, such as sharing notification contents and deciding on courses of action. Findings highlight how embodied and technological resources are collectively drawn upon in situationally nuanced ways to achieve the management of notifications delivered to cohorts. The insights can be used to develop an understanding of how interruptions are dealt with in other settings, and to reflect on how to support notification management within collocated groups by design.

show abstract

“…In [17] the authors propose a context-aware and personalized mobile recommender system for young people in leisure time. The system predicts the user's current and future leisure activity (eating, seeing, reading, doing, and shopping) from context (time, location) and patterns of user behavior.…”

Section: Related Workmentioning

confidence: 99%

“…-Our approach is implicit and automatic; no effort is needed from the user, while in [12], [16] the user is solicited in the process of building his profile. -Our approach does not take any restriction on user's situations or population, while in [17] it is devoted to some specific situations and specific populations.…”

Section: Related Workmentioning

confidence: 99%

Dynamically Personalizing Search Results for Mobile Users

Bouidghaghen¹,

Tamine-Lechani²

2009

Flexible Query Answering Systems

View full text Add to dashboard Cite

Abstract. We introduce a novel situation-aware approach to personalize search results for mobile users. By providing a mobile user with appropriate information that dynamically satisfies his interests according to his situation, we tackle the problem of information overload. To build situation-aware user profile we rely on evidence issued from retrieval situations. A retrieval situation refers to the spatio-temporal context of the user when submitting a query to the search engine. A situation is represented as a combination of geographical and temporal concepts inferred from concrete time and location information by some ontological knowledge. User's interests are inferred from past search activities related to the identified situations. They are represented using concepts issued from a thematic ontology. We also involve a method to maintain the user's interests over his ongoing search activity and to personalize the search results.

show abstract

Activity-based serendipitous recommendations with the Magitti mobile leisure guide

Cited by 138 publications

References 32 publications

Predicting User Locations and Trajectories

Predicting User Locations and Trajectories

Understanding Mobile Notification Management in Collocated Groups

Dynamically Personalizing Search Results for Mobile Users

Contact Info

Product

Resources

About