Using function approximation for personalized point-of-interest recommendation
TL;DR: A method to automatically adjust the weights according to users personal preference is proposed, where the Chebyshev polynomial approximation method using binary values is applied and it is shown that the new approach can make a good balance of weighting the two factors therefore providing better recommendation.
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Abstract: Point-of-interest (POI) recommender system encourages users to share their locations and social experience through check-ins in online location-based social networks. A most recent algorithm for POI recommendation takes into account both the location relevance and diversity. The relevance measures users personal preference while the diversity considers location categories. There exists a dilemma of weighting these two factors in the recommendation. The location diversity is weighted more when a user is new to a city and expects to explore the city in the new visit. In this paper, we propose a method to automatically adjust the weights according to users personal preference. We focus on investigating a function between the number of location categories and a weight value for each user, where the Chebyshev polynomial approximation method using binary values is applied. We further improve the approximation by exploring similar behavior of users within a location category. We conduct experiments on five real-world datasets, and show that the new approach can make a good balance of weighting the two factors therefore providing better recommendation.
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Figures
Table 1: Sample of User Check-in Sequences 
Fig. 1: The trend for users to explore location categories over time 
Table 2: Statistics of the check-in data 
Fig. 4: Performance of forecasting category numbers for three users in Austin 
Fig. 3: Overall performance of forecasting category numbers 
Fig. 7: Effect of Varying Number of Chebyshev Polynomials
Citations
Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey
TL;DR: This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area.
Multi-objective item evaluation for diverse as well as novel item recommendations
TL;DR: This work incorporates Bhattacharyya Coefficient in an existing nonlinear similarity computation model to create a new similarity model named as Bhat_sim to increase the prediction accuracy of the exiting rating evaluation methods and formulate a multi-parent crossover mechanism NewCross in the proposed multi-objective recommendation filtering NewCrossPMOEA which preserves the order and the frequency in the parents genes to bring good objectivity.
46
Tourism recommendation system: a survey and future research directions
TL;DR: Various Artificial Intelligence (AI) techniques have been highlighted which are used to solve the tourist recommendation problem and future research direction has been suggested which would improvise the Quality of Service (QoS) of the RS in tourism industry.
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Crowd management COVID-19.
TL;DR: In this article, the authors present a solution for crowd management based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user.
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A new point-of-interest approach based on multi-itinerary recommendation engine
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24
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