An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A music recommendation system based on music data grouping and user interests
Proceedings of the tenth international conference on Information and knowledge management
The role of adaptive hypermedia in a context-aware tourist GUIDE
Communications of the ACM - The Adaptive Web
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Developing a location based tourist guide application
ACSW Frontiers '03 Proceedings of the Australasian information security workshop conference on ACSW frontiers 2003 - Volume 21
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Review: Personalizing recommendations for tourists
Telematics and Informatics
Event-based communication for location-based service collaboration
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
Recommendation of little known good travel destinations using word-of-mouth information on the web
AMT'10 Proceedings of the 6th international conference on Active media technology
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Personalized recommendations in a mobile tourist information system suffer from a number of limitations Most pronounced is the amount of initial user information needed to build a user model In this paper, we adopt and extend the basic concepts of recommendation paradigms by exploiting a user's personal information (e.g., preferences, travel histories) to replace the missing information The designed algorithms are embedded as recommendation services in our TIP prototype We report on the results of our analysis regarding effectiveness and performance of the recommendation algorithms We show how a number of limiting factors were successfully eliminated by our new recommender strategies.