Exploiting User Movements to Derive Recommendations in Large Facilities

This paper provides an innovative approach for taking advantage of user’s movement data as implicit user feedback for deriving recommendations in large facilities. By means of a real-world museum scenario a beacon infrastructure for tracking sojourn times is presented. Then we show how sojourn times can be integrated in a collaborative filtering algorithm approach in order to outcome accurate recommendations.

[1]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[2]  Sascha Ossowski,et al.  A Proposal for Situation-Aware Evacuation Guidance Based on Semantic Technologies , 2016, EUMAS/AT.

[3]  Kin K. Leung,et al.  Context-Awareness for Mobile Sensing: A Survey and Future Directions , 2016, IEEE Communications Surveys & Tutorials.

[4]  Linas Baltrunas,et al.  Towards Time-Dependant Recommendation based on Implicit Feedback , 2009 .

[5]  Angelo Chianese,et al.  SmARTweet: A Location-Based Smart Application for Exhibits and Museums , 2013, 2013 International Conference on Signal-Image Technology & Internet-Based Systems.

[6]  Sascha Ossowski,et al.  A distributed architecture for real-time evacuation guidance in large smart buildings , 2017, Comput. Sci. Inf. Syst..

[7]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[8]  Andrea Bottino,et al.  MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a Museum , 2013, Sensors.

[9]  Fabio Paternò,et al.  UbiCicero: A location-aware, multi-device museum guide , 2009, Interact. Comput..

[10]  Young Park,et al.  A time-based approach to effective recommender systems using implicit feedback , 2008, Expert Syst. Appl..

[11]  Ramón Hermoso,et al.  Situation Awareness for Push-Based Recommendations in Mobile Devices , 2016, BIS.

[12]  Idir Benouaret,et al.  Personalizing the Museum Experience through Context-Aware Recommendations , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[13]  Francesco Ricci,et al.  Context-Aware Recommender Systems , 2011, AI Mag..

[14]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.