Personalized multi-period tour recommendations

Abstract

During a trip planning, tourists gather information from different sources, select and rank the places to visit according to their personal interests, and try to devise daily tours among them. This paper addresses the complex selection and touring problem and proposes a "filter-first, tour-second" framework for generating personalized tour recommendations for tourists based on information from social media and other online data sources. Collaborative filtering is applied to identify a subset of optional points of interest that maximize the potential satisfaction, while there are some preselected mandatory points that the tourists must visit. Next, the underlying orienteering problem is solved via an Iterated Tabu Search algorithm. The goal is to generate tours that contain all mandatory points and maximize the total score collected from the optional points visited daily, taking into account different day availabilities and opening hours, limitations on the tour lengths, budgets and other restrictions. Computational experiments on benchmark datasets indicate that the proposed touring algorithm is very competitive. Furthermore, the proposed framework has been evaluated on data collected from Foursquare. The results show the practical utility and the temporal efficacy of the recommended tours.

Exploring Tourist Dining Preferences Based on Restaurant Reviews

“…Tourism practitioners can develop applications to monitor tourist preferences for food and their sentiment towards food providers in real-time. Mobile recommendation applications can also be developed to provide tourist with suitable dining offer while traveling (Kotiloglu et al 2017). …”

Smartphone-based tourist trip planning system: a context-based approach to offline attraction recommendation

“…The paper [12] offers the «Filter-first, tour-second» approach for creating personalized recommendations on tourist routes, based on information from social networks and other online data sources. The collaborative filtering technique is used to define a subset of additional points of interest that maximize potential user satisfaction from the route.…”

“…The authors of papers [4,7,9,12] does not work with context information during attraction route visiting at all, authors of paper [11] formalizes context only as user location (city and coordinates), [14] context does not contain traffic information, [10] context contains only a topological cafeterias, [5,6] does not give many details about route planning and more focusing on ontological method for attraction processing and the authors of [8] describes only a recommendation tourism system without route planning. The proposed method in system will take into account the situation on the road, including traffic jams, current weather and season, attractiveness of sights and their schedules.…”

Travel Package Recommendation System based on Package Locations and Rating

“…Moreover, the proposed structure has been assessed on information gathered from Foursquare. [1]Users are equipped for getting a genuine inclination utilizing VR. Client now invest his quality time without squandering their vitality in finding appropriate aides.…”