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.
- Cost-aware collaborative filtering
- Tour recommendation
ASJC Scopus subject areas
- Information Systems
- Business, Management and Accounting(all)
- Computer Science Applications