Sem-ppa

TL;DR: In this paper, a systematic review of the e-tourism management system applied to the smart tourism concepts over the last eight years of publication is presented, in which the authors provide a state-of-the-art taxonomy based on smart concepts and reviews works in different fields against that classification.

TL;DR: A decision‐making framework on the basis of the extension of interval type 2 trapezoidal‐fuzzy weighted with zero inconsistency (IT2TR‐FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e‐tourism data management applications is presented.

TL;DR: Wang et al. as mentioned in this paper proposed a deep distributed learning-based POI recommendation (Deep-PR) method for situations of mobile edge networks, in which hidden feature components from both local and global subspaces are deeply abstracted via representative learning schemes.

TL;DR: A personalized travel package recommendation approach to generate dynamic recommendations to the users by taking into account the multidimensional data of time, location, users’ implicit ratings, tourists’ characteristics and their sequential travel movement patterns based on the geo-tagged photos.

TL;DR: The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.

TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.

TL;DR: This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.

TL;DR: This paper presents a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters and shows that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset.