Andrew Ponomarev

Recommendation system for tourist attraction information service

The paper proposes a description of information decision support system in the tourism domain and a set of methods and algorithms for generating recommendations for a user that allow significant increase of the system usability. The system generates for the user recommendations which attractions at the moment are better to attend based on the user preferences and the current situation in the location area. The system also allows showing the user information about interesting attraction in more detail, which is based on analyzing information evaluations made by other users.

Smart Space-Based Tourist Recommendation System

The paper presents smart space-based tourist recommendation system that allows acquiring information about places of interests around the tourist from different internet sources (like wikipedia, wikivoyage, wikitravel, panoramio, flickr). The system implements ranking acquired attractions accord- ing to the tourist preferences and current situation in the tourist location. Tour- ists can rate attractions that they like or dislike. Based on these ratings a rec- ommendation service clusters tourists into groups with similar interests and us- es evaluations of tourists belonging to the same group for ranking attractions around the tourist. The paper presents a prototype service for these purposes that is based on smart space technology. The prototype has been developed for Android devices and is available for free downloaded from Google Play market.

Context-based infomobility system for cultural heritage recommendation: Tourist Assistant--TAIS

The paper presents an infomobility system (Tourist Assistant—TAIS) for supporting tourists in a region. The system solves both main tasks related to the infomobility concept: user actions analysis, preferences revealing, cultural heritage recommendation based on the preferences and current situation in the region, and provides the user with possible transportation means to reach the corresponding cultural heritage. Multimedia information about the cultural heritage includes: text description and images, and videos extracted from accessible Internet sources (such as Wikipedia, Wikivoyage, Panoramio and YouTube). The system consists of a set of services joined together by a smart space that provides possibilities to organize semantic-based information exchange between these services using ontology-based approach. Services share information for joint solving the tourist’s task with the smart space. This information forms cultural space that is a model of physical space where the tourist is. The list of cultural heritage provided to the tourist is ordered by the special recommendation service that implements the ranking based on the collaborative filtering technique. Recommendations are based on ratings set by the tourists that use the system. To provide the tourist with different transportation means, the special transportation service has been developed. This service calls a taxi, finds ridesharing possibilities or calculates multimodal route by public transport based on the tourist preferences. The paper describes the service-based system architecture, services development, their ontologies, and implementation and evaluation. The prototype of the developed system is accessible for download in the Google Play market for Android device users.

Cyber-physical infomobility for tourism application

The paper proposes an approach, reference model, and case study for a cyber-physical infomobility for tourism application. The logistics system of the hub is considered as a cyber-physical system. The main idea behind the virtual tourist hub concept is to arrange a tourist trip based on the available schedules, capabilities and environment-friendliness of transportation and attraction providers, current and foreseen availability and occupancy of the available transportation means and attraction services. The approach assumes usage of a context-based group recommending system for generating recommendations for the tourists. A case study describes service-oriented architecture, implementation, and evaluation of mobile tourist guide application that is based on the presented in the paper approach and reference model.

Smart space-based intelligent mobile tourist guide: Service-based implementation

The paper presents an intelligent mobile tourist guide architecture that allows tourists to get information about attractions around the current geographic location based on tourist context and estimations of other tourists. Information about attractions (images and description blocks) is extracted from different internet services (like Wikipedia, Wikivoyage, Wikitravel, Panoramio, Flickr) “on the fly” that allows to use mobile tourist guide in any region of the world and get actual at the moment information. The mobile tourist guide provides information about public transport and car sharing possibilities for the tourist with drivers nearby for comfortable reaching preferred attractions. It is based on the smart space technology that provides possibilities of personal devices and different services seamless integration. The paper also discusses services interaction in smart space that allows to implement majority of mobile tourist guide tasks in computational power devices and use personal mobile devices only for results visualization.

Proactive Recommendation System for m-Tourism Application

In m-tourism applications, the proactive recommendations are especially actual for two major reasons: (1) the highly dynamic nature of the problem situation (the user continuously moves, the transport situation and weather conditions change); (2) limited possibilities of mobile devices for explicit information entry and checking large amounts of alternative solutions, but rich possibilities for tacit information entry via various sensors. The paper proposes an approach and research prototype based on the technologies of smart space and proactive recommendation systems. The architecture is based on the smart space technology. The system implementing the proposed approach helps the tourists to plan their attraction attending schedule based on the context information about the current situation in the region, its foreseen development, the tourist’s preferences and previous behavior, using their mobile devices.

Time Series Forecasting: Applications to the Upstream Oil and Gas Supply Chain

Abstract This paper describes different models which are used for forecasting in the time series context of petroleum engineering. The objective is to reproduce and further predict future oil production in different scenarios in an adjustable time window. Such time series are very similar to those from the sequential manufacturing processes which are usual in many areas of manufacturing industries. We mainly focus on a feedforward neural network model and a Gamma classifier and compare them both on a benchmark and real industrial data under univariate and multivariate settings. While the former model has become recently a standard tool for modeling and prediction, time series forecasting is not the kind of tasks envisioned while designing and developing the Gamma model. The Gamma classifier is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. As experimental results show, pattern recognition based classifier shows very competitive performance. The advantages and limitations of each model are discussed.

A multimodel context-aware tourism recommendation service: Approach and architecture

Recommender systems and services are now widely used to support decision-making in the fields characterized by the selection from a large number of alternatives with a significant influence of subjective preferences. A comprehensive multimodel approach to the development of context-aware recommender systems in the field of tourism information support is proposed. In particular, it is proposed to construct a recommender system based on loosely coupled modules, in which both personalized and nonpersonalized recommendation methods are implemented, and the synthesis module, which adapts the module system to the specific conditions of different kinds of initial information.

Application for e-Tourism: Intelligent Mobile Tourist Guide

This paper presents an e-Tourism application for supporting tourists in a region. The application recommends the tourist attractions that are interested to him/her based on the tourist preferences and the current situation in the region. Attractions and their descriptions & images are extracted from accessible Internet sources (like Wikipedia, Wikivoyage, Panoramio). They are ranged by the special recommendation service of the application. Recommendations are based on ratings set by the tourists that use the application. The paper describes the service-based application architecture, ontology for intelligent mobile tourist guide services interaction, and evaluation. Developed application is accessible for download in Google Play market for Android device users.

Tourist Attraction Recommendation Service: An Approach, Architecture and Case Study

The paper proposes a complex multi-model approach to recommendation systems design in the domain of tourist information support. Specifically, it proposes to construct a recommendation system as a composition of loosely coupled modules, implementing both personalized and non-personalized methods of recommendations and a coordination module responsible for adaptation of the whole system to the specific tourist and situation context.