Season-aware Attraction Recommendation Method with Dual-trust Enhancement

Attraction recommendation plays an essential role in tourism. For example, it can relieve information overload for tourists and increase sales for tourism operators. When making travel decisions, tourists depend heavily on the personal preferences and suggestions from people they trust. However, most existing attraction recommendation methods focus on the tourist preferences for topics of attractions, yet overlook the seasonality in topic preferences. Additionally, extant studies are generally based on a single type of trust, which may represent trust relations inaccurately. In order to overcome these issues, we propose a novel season-aware attraction recommendation method based on the seasonal topic preferences and dual-trust relations. Firstly, we capture tourists’ seasonal topic preferences by analyzing their travel histories along two dimensions: time and attraction. Secondly, we develop a dual-trust relationship (DTR) model based on familiarity-based trust and similarity-based trust, in contrast to existing studies that purely focus on a single type of trust. Thirdly, we propose a novel season-aware attraction recommendation method named SAR-DTR. In a specific season, it predicts ratings based on both topic preferences in the given season and suggestions from tourists they trust. To demonstrate the superiority of the proposed method to other approaches, an empirical study with real-world data was conducted. The experimental results regarding both prediction and recommendation performance are reported

A model for a socially-aware mobile tourist recommendation system

People, whether for business or pleasure, often travel or visit locations they are unfamiliar with. To overcome this, a mobile tourist recommendation system can be used. This system is able to generate tourist attraction recommendations for a person, based on a number of criteria such as time and location. One criterion often overlooked though, is a person's social environment. This is an important consideration as touring is often done with more than one person. By not considering the other people within a person's social environment, recommendations will only be limited to the preferences of a single person. The purpose of this paper is to propose a model for a mobile tourist guide recommendation system that considers a persons social environment when generating tourist attraction recommendations. This is done by studying other tourist recommendation systems, eliciting key requirements for a socially aware tourist guide system from them, and using these requirements as input for a mobile tourist recommendation system model that can harness a person's social environment when recommending tourist attractions

Travel recommendations in a mobile tourist information system

An advanced mobile tourist information system delivers information about sights and events on a tourists travel route. The system should be personalized in its interaction with the tourist. Data that can be used for personalization are: the tourists interest profile, an analysis of their travel history, and the tourists feedback about sights. Existing mobile information systems for tourists do not tailor their information delivery to the tourists interests. In this paper, we propose the use of personalised recommendations that consider all of the personal information a tourist provides. We adopt and modify techniques from recommended systems to the new application area of mobile tourist information. We propose a number of methods for personalised recommendations; and select a subset of these for implementation. This paper then presents the implemented recommended component of our TIP system for mobile tourist informatio

Advanced recommendations in a mobile tourist information system

An advanced tourist information provider system delivers information regarding sights and events on their users' travel route. In order to give sophisticated personalized information about tourist attractions to their users, the system is required to consider base data which are user preferences defined in their user profiles, user context, sights context, user travel history as well as their feedback given to the sighs they have visited. In addition to sights information, recommendation on sights to the user could also be provided. This project concentrates on combinations of knowledge on recommendation systems and base information given by the users to build a recommendation component in the Tourist Information Provider or TIP system. To accomplish our goal, we not only examine several tourist information systems but also conduct the investigation on recommendation systems. We propose a number of approaches for advanced recommendation models in a tourist information system and select a subset of these for implementation to prove the concept

Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

Personalized Ranking for Context-Aware Venue Suggestion

Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper we present a novel user-modeling approach which relies on a set of scoring functions for making personalized suggestions of venues based on venues content and reviews as well as users context. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, prove that our methodology outperforms state-of-the-art approaches by a significant margin.Comment: The 32nd ACM SIGAPP Symposium On Applied Computing (SAC), Marrakech, Morocco, April 4-6, 201