Developing a context-aware electronic tourist guide: some issues and experiences
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Ubiquitous User Assistance in a Tourist Information Server
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adapting the right measures for K-means clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
A case study on the effectiveness of recommendations in the mobile internet
Proceedings of the third ACM conference on Recommender systems
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
Hybrid web recommender systems
The adaptive web
Equip tourists with knowledge mined from travelogues
Proceedings of the 19th international conference on World wide web
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
An energy-efficient mobile recommender system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Unifying explicit and implicit feedback for collaborative filtering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Electronic mobile guides: a survey
Personal and Ubiquitous Computing
Context relevance assessment for recommender systems
Proceedings of the 16th international conference on Intelligent user interfaces
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Cost-aware travel tour recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A model for proactivity in mobile, context-aware recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Collaborative filtering with collective training
Proceedings of the fifth ACM conference on Recommender systems
Location-based recommendation system using Bayesian user's preference model in mobile devices
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data could be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the tourist’s choice is affected directly by the travel costs, which includes both financial and time costs. To that end, in this article, we provide a focused study of cost-aware tour recommendation. Along this line, we first propose two ways to represent user cost preference. One way is to represent user cost preference by a two-dimensional vector. Another way is to consider the uncertainty about the cost that a user can afford and introduce a Gaussian prior to model user cost preference. With these two ways of representing user cost preference, we develop different cost-aware latent factor models by incorporating the cost information into the probabilistic matrix factorization (PMF) model, the logistic probabilistic matrix factorization (LPMF) model, and the maximum margin matrix factorization (MMMF) model, respectively. When applied to real-world travel tour data, all the cost-aware recommendation models consistently outperform existing latent factor models with a significant margin.