Influence-Time-Proximity Driven Locations Recommendation Model: An Integrated Approach
01 Oct 2019-pp 1381-1386
TL;DR: This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations to generate a ranked list of locations which will be recommended to the user.
Abstract: Location Based Social Networks (LBSNs) like Twitter, Foursquare or Instagram are a very good source of extracting human generated data in the form of check-ins, location and social relationships among users. Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users preference and behaviour. There are many underlying patterns in this type of dataset of human mobility which are utilised for applications like recommender systems. The current approaches involve extracting data from user-item rating, GPS trajectories, or other forms of data, whereas we focus on an integrated model considering factors like user's interest, social influence, time and proximity. There is metadata associated with this dataset which tells us about the whereabouts of the user, with emphasis on types of places. This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations. We integrate all these parameters to generate a ranked list of locations which will be recommended to the user. Experiments are performed on a real-world dataset which show that our proposed model is effective in the stated conditions.
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TL;DR: In this paper, the authors presented a novel, complete and consistent dataset describing tourist density at high spatial resolution with monthly breakdown for the whole of the European Union, which is achieved thanks to the integration of data from conventional statistical sources with big data from emerging sources, namely two major online booking services containing the precise location and capacity of tourism accommodation establishments.
128 citations
"Influence-Time-Proximity Driven Loc..." refers background in this paper
...v Seasonality- The seasonality of a category depicts the monthly variation of check-ins by the users, in a particular category [14] [15]....
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TL;DR: In this paper, two forecasting models, RIMA14 and ARIMA1, are used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity.
Abstract: Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, RIMA14 and ARIMA1. ARIMA14 is used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity. ARIMA1 considers the series only in first differences, and seasonality is modeled with a constant and three seasonal dummies. The selection of either model depends on the nature of seasonality. Conventional unit root tests determine the nature of seasonality and the order of integration and, therefore, the series' choice of forecasting model. To determine whether the test correctly identifies the forecasting model for tourism demand, out-of-sample forecasting performance of ARIMA1 and ARIMA14 is compared with HEGY unit root model selection method. Comparing forecasting performance of both models with HEGY unit root model selection shows that the outcome of HEGY test procedure may not be useful in the selection of a univariate time-series model for quarterly tourism demand series.
116 citations
"Influence-Time-Proximity Driven Loc..." refers background in this paper
...v Seasonality- The seasonality of a category depicts the monthly variation of check-ins by the users, in a particular category [14] [15]....
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TL;DR: It is found that multi-label affordance estimation is not straightforward but can be made to work using both official webtexts and user-generated content on a medium semantic level, which opens up new opportunities for data-driven approaches to urban leisure and tourism studies.
25 citations
09 May 2018
TL;DR: A personalized tourist attraction recommendation mechanism is designed in terms of different factors, including user preference, social relationship, location distance and location popularity, which ranks tourist attractions based on weighted average by combining the above three scores.
Abstract: 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
10 citations
TL;DR: In this article, the authors analyzed patterns and effects of seasonality of tourism demand in Petra for the period 2006-2017 and found that there are two peaks in tourism demand of Petra, the first and the highest one was in April and the second took place in October and November, respectively.
Abstract: Petra is a mature tourism destination in the south of Jordan with a degree of seasonality over the last 10 years. Despite the recognized importance of Petra for the tourism industry in Jordan, there has been a lack of studies that discuss seasonal demand variations and its impacts on other related industries in the region. Yet, this study aims at analyzing patterns and effects of seasonality of tourism demand in Petra for the period 2006-2017. The required data was obtained from Ministry of Tourism and Antiquities (MoTA). Four methods were used to measure tourism seasonality in Petra. These are: Seasonality indicator; Seasonality ratio; Gini coefficient; and Seasonality index. The results of the study showed a modest level of tourism seasonality in the study area. Among methods, Seasonality index appeared to be the appropriate and simple way to calculate seasonality patterns at tourism destinations. The results showed that there are two peaks of seasonality in tourism demand of Petra. The first and the highest one was in April and the second took place in the months of October and November. In addition, seven months represented the low season of tourism demand in Petra. These are December, January and February as well as June, July, August and September. The tourism seasonality in Petra based on that is mainly due to the weather in these months which represent the coldest and warmest months in the year respectively.
4 citations
"Influence-Time-Proximity Driven Loc..." refers background or methods in this paper
...In [3] and [4], the different approaches to measure the seasonal variations in tourism are described and compared....
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...The seasonality index: is calculated through the following steps [3]:...
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