This paper proposes a new method to provide personalized tour recommendation for museum visits. It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy. This project includes researchers from computer and social sciences. Some results are obtained with numerical experiments. They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour. This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.
Relying on the constraint programming model @cite_13 , we propose to reduce the number of used variables. In @cite_13 , they generate a route by calculating the smallest number @math of steps required to cross the museum (to visit all the rooms). This model requires that each artwork is represented as @math variables (one per step). Due to the fact that museums often have several thousands of artworks, it leads to a huge number of variables. Moreover they use mathematical distributions to simulate a visitor profile which does not necessary reflect reality (in museums, artworks are often grouped in a room because they are related to each other, a configuration that a random distribution as they used cannot represent).
@cite_13
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We study the computational complexity of the art gallery problem originally posed by Klee, and its variations. Specifically, the problem of determining the minimum number of vertex guards that can see an n -wall simply connected art gallery is shown to be NP-hard. The proof can be modified to show that the problems of determining the minimum number of edge guards and the minimum number of point guards in a simply connected polygonal region are also NP-hard. As a byproduct, the problem of decomposing a simple polygon into a minimum number of star-shaped polygons such that their union is the original polygon is also shown to be NP-hard. In the museum visitor routing problem (MVRP), each visitor group has some exhibit rooms of interest. The visiting route of a certain visitor group requires going through all the exhibit rooms that the group wants to visit. Routes need to be scheduled based on certain criteria to avoid congestion and or prolonged touring time. In this study, the MVRP is formulated as a mixed integer program which is an extension of the open shop scheduling (OSS) problem in which visitor groups and exhibit rooms are treated as jobs and machines, respectively. The time each visitor group spends in an exhibit room is analogous to the processing time required for each job on a particular machine. The travel time required from one exhibit room to another is modeled as the sequence-dependent setup time on a machine, which is not considered in the OSS problem. Due to the intrinsic complexity of the MVRP, a simulated annealing (SA) approach is proposed to solve the problem. Computational results indicate that the proposed SA approach is capable of obtaining high quality MVRP solutions within a reasonable amount of computational time and enables the approach to be used in practice. In this paper, we consider the problem of designing personalised museum visits. Given a set of preferences and constraints a visitor might express on her visit, the aim is to compute the tour that best matches her requirements. The museum visits problem can be expressed as a planning problem, with cost optimization. We show how to bound the number of steps required to find an optimal solution, via the resolution of an instance of the shortest complete walk problem. We also point out an alternative encoding of the museum visits problem as an optimization problem with pseudo-Boolean constraints and a linear objective function. We have evaluated several constraints solvers, a planner and a tailored solver on a number of benchmarks, representing various instances of the museum visits problem corresponding to real museums. Our empirical results show the feasibility of both the planning and the constraint programming approaches. Optimal solutions can be computed for short visits and "practically good" solutions for much longer visits. Vast amounts of artistic data is scattered on-line from both museums and art applications. Collecting, processing and studying it with respect to all accompanying attributes is an expensive process. With a motivation to speed up and improve the quality of categorical analysis in the artistic domain, in this paper we propose an efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain. We continue to show how different multi-task configurations of our method behave on artistic data and outperform handcrafted feature approaches as well as convolutional neural networks. In addition to the method and analysis, we propose a challenge like nature to the new aggregated data set with almost half a million samples and structured meta-data to encourage further research and societ al engagement.