Latent semantic models for collaborative filtering
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Item-based top-N recommendation algorithms
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Scalable collaborative filtering using cluster-based smoothing
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Supporting Context-Aware Media Recommendations for Smart Phones
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Developing recommender systems with the consideration of product profitability for sellers
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Factorization meets the neighborhood: a multifaceted collaborative filtering model
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Regression-based latent factor models
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Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
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A spatio-temporal approach to collaborative filtering
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Unified video annotation via multigraph learning
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Equip tourists with knowledge mined from travelogues
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Context relevance assessment for recommender systems
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Collaborative filtering with collective training
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Gaussian process for recommender systems
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Collaborative filtering with user ratings and tags
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Constructing trip routes with user preference from location check-in data
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Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
Customized tour recommendations in urban areas
Proceedings of the 7th ACM international conference on Web search and data mining
Div-clustering: Exploring active users for social collaborative recommendation
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Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data can 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 directly affected by the travel cost, which includes the financial cost and the time. To that end, in this paper, we provide a focused study of cost-aware tour recommendation. Along this line, we develop two cost-aware latent factor models to recommend travel packages by considering both the travel cost and the tourist's interests. Specifically, we first design a cPMF model, which models the tourist's cost with a 2-dimensional vector. Also, in this cPMF model, the tourist's interests and the travel cost are learnt by exploring travel tour data. Furthermore, in order to model the uncertainty in the travel cost, we further introduce a Gaussian prior into the cPMF model and develop the GcPMF model, where the Gaussian prior is used to express the uncertainty of the travel cost. Finally, experiments on real-world travel tour data show that the cost-aware recommendation models outperform state-of-the-art latent factor models with a significant margin. Also, the GcPMF model with the Gaussian prior can better capture the impact of the uncertainty of the travel cost, and thus performs better than the cPMF model.