Travelling is a combination of journey, transportation, travel-time, accommodation, weather, events, and other aspects which are likely to be experienced by most of the people at some point in their life. To enhance such experience, we generally look for assistance in planning a tour. Today, the information available on tourism-related aspects on the Internet is boundless and exploring suitable travel package/product/service may be time-consuming. A recommender system (RS) can assist for various tour-related queries such as top destinations for summer vacation, preferable climate conditions for tracking, the fastest way to transport, or photography assistance for specific destinations. In this survey, we have presented a pervasive review on travel and associated factors such as hotels, restaurants, tourism package and planning, and attractions; we have also tailored recommendations on a tourist’s diverse requirements such as food, transportation, photography, outfits, safety, and seasonal preferences. We have classified travel-based RSs and presented selection criteria, features, and technical aspects with datasets, methods, and results. We have briefly supplemented research articles from diverse facets; various frameworks for a travel-based RS are discussed. We believe our survey would introduce a state-of-the-art travel RS; it may be utilized to solve the existing limitations and extend its applicability.

A Comprehensive Survey on Travel Recommender Systems

A point of interest (POI) recommender system (RS) is one of the representative research areas based on the location-based social network (LBSN). Most POI RS studies utilized various implicit information or social information to improve recommendation accuracy. However, majority of these studies overlooked the importance of users’ initial check-in information. Users are affected by their first input data in online services, and this phenomenon is called the anchoring effect. In POI RSs, few studies have analyzed the association with the anchoring effect while other RS domains already verified this effect. In particular, a research area, including POI RS, that focuses on the importance of the initial input does not exist. In this paper, we propose a latent Dirichlet allocation (LDA) model based on the anchoring effect for POI RS. This model emphasizes the importance of initial check-in data and is called the anchor-LDA. Experimental results showed that the anchor-LDA outperformed existing LDA-based POI recommender algorithms. Furthermore, we validated the importance of initial check-in information on the LBSN.

Point of interest recommendations based on the anchoring effect in location-based social network services

With the development of information technology and the popularity of the Internet, the data on the network is growing exponentially. Information overload has become a significant issue for consumers seeking information. A recommendation system was created to detect users’ interests from huge amounts of data and to suit users’ specific information needs. Traditional collaborative filtering recommendation mostly uses scoring data for a recommendation, which has the problem of sparse data, which limits the performance of the recommendation system. On this basis, this paper studies the personalized recommendation algorithm of scenic spots with deep migration. Through the analysis of collaborative filtering recommendation methods, it is found that the traditional collaborative filtering methods only use scoring data for a recommendation, which has the problem of sparse data. Based on the vectorization of user interest, the similarity of user preference is calculated, and the matrix decomposition is carried out in cooperation with user implicit feedback, to integrate the knowledge transfer information into the matrix decomposition model, and make up for the lack of considering the attribute information of scenic spots in the matrix decomposition algorithm, and alleviate the problem of data sparsity. The findings of comparative trials suggest that the personalized scenic location recommendation approach proposed in this study, which is based on the depth migration algorithm, is effective. Compared with the benchmark recommendation method, the recommendation accuracy and recall rate has been improved to a certain extent.

Personalized Recommendation Algorithm of Tourist Attractions Based on Transfer Learning

The spatial structure of tourist attractions can be presented both qualitatively and quantitatively. One of the indicators of the spatial structure of tourism is the index of geographical concentration of tourist attractions. The geographical concentration of tourist attractions represents the ratio of the number of tourist attractions in the observed area and its structural parts and the total number of structural units of the analyzed area. This paper aims to determine the spatial distribution of attractions in the administrative territories of Belgrade municipalities and to establish correlations with tourist attendance. The number and spatial distribution of accommodation capacities are the largest in the central city municipalities so that the number of visitors is the largest in them. At the same time, the central city municipalities have the highest concentration of tourist attractions. For data collection, the authors used field research, OSM (Open Street Maps), Google maps, with software processing ArcGIS 10.2. The research results enabled the definition of the model of distribution of tourist attractions and indicated its application. This model of distribution of tourist attractions shows that they are mostly concentrated in the city center. This also means a small spatial connection of tourist attractions in the city center and peripheral parts.

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Geographical index of concentration as an indicator of the spatial distribution of tourist attractions in Belgrade

With the continuous development of mobile positioning technology and smart phones, users can use smart phones to obtain and share location information of themselves and various surrounding points of interest (POI) anytime, anywhere, and share their own activity information, thus forming a location-based social network (LBSN). A large amount of user data is generated in LBSN. How to quantitative analyze the effects of context to manager’s view, how to extract the hidden user feature through the analysis of user data is of important research significance for the intelligent analysis of user characteristics. In this paper, first, a muti-dimensional user feature construction method is proposed, which extracts user feature from different influencing factors. Second, the fitness of user to a feature is analyzed. Third, a unified model is used to characterize this applicability. The method can promote the transformation from user data to user feature and help solve the problem of “explosive data but poor knowledge”. Experimental verification shows that the method is feasible and can realize the mining of muti-dimensional feature of users.

Data driven user feature prediction in mobile applications based on multi-channel CNN

With the development of the tourism, vast amount of tourist attraction information makes it complex and time-consuming for users to obtain satisfactory travel destination Rich topological, temporal and spatial information in Location-Based Social Network (LBSN) helps to mine user preference deeply and evokes effectiveness of tourist attraction recommendation In this paper, a personalized tourist attraction recommendation mechanism is designed in terms of different factors, including user preference, social relationship, location distance and location popularity Firstly, a large number of check-ins in LBSN are filtered for further geographical space clustering to get real tourist attraction check-ins by DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm Then, a location-aware LDA model is used to excavate user's latent preference topic distribution and region's latent topic distribution on tourist attractions After that, these two attributes together with social relationship are used to make user interest scoring function Furthermore, another two scoring functions are established respectively based on tourist attraction location distance and popularity At last, a personalized tourist attraction recommendation algorithm is designed to recommend tourist attractions to users, which ranks tourist attractions based on weighted average by combining the above three scores Experiments are carried out on Foursquare data sets and results show that our method can perform well on recommendation in home city and new city

Ziqing Zhu

Papers

Location-Time-Sociality Aware Personalized Tourist Attraction Recommendation in LBSN