7,141 research outputs found
Capturing the Visitor Profile for a Personalized Mobile Museum Experience: an Indirect Approach
An increasing number of museums and cultural institutions
around the world use personalized, mostly mobile, museum
guides to enhance visitor experiences. However since a typical
museum visit may last a few minutes and visitors might only visit
once, the personalization processes need to be quick and efficient,
ensuring the engagement of the visitor. In this paper we
investigate the use of indirect profiling methods through a visitor
quiz, in order to provide the visitor with specific museum content.
Building on our experience of a first study aimed at the design,
implementation and user testing of a short quiz version at the
Acropolis Museum, a second parallel study was devised. This
paper introduces this research, which collected and analyzed data
from two environments: the Acropolis Museum and social media
(i.e. Facebook). Key profiling issues are identified, results are
presented, and guidelines towards a generalized approach for the
profiling needs of cultural institutions are discussed
The Cowl - v.78 - n.8 - Oct 31, 2013
The Cowl - student newspaper of Providence College. Vol 78 - No. 8 - October 31, 2013. 24 pages
Group Modeling : selecting a sequence of television items to suit a group of viewers
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Personalized ranking metric embedding for next new POI recommendation
The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods
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