Trip Recommendation Meets Real-World Constraints

Zhang, Chenyi; Liang, Hongwei; Wang, Ke

doi:10.1145/2948065

Cited by 32 publications

(12 citation statements)

References 39 publications

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“…Yuan et al [10] found out that most users tend to visit different POIs at a different time in a day, and the check-in behaviors between neighbor time slots are similar. In addition to that, Zhang et al [27] pointed out that POI may be not available in all time, for example, POIs are only accessible during their opening hours. He et al [28] investigated the temporal popularity of a POI and the temporal check-in trends to provide personalized POI recommendation.…”

Section: Related Workmentioning

confidence: 99%

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

2021

Wireless Communications and Mobile Computing

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The POI recommendation system has become an important means to help people discover attractive and interesting places. Based on our data analysis, we observe that users pay equal attention to conservatism and curiosity. In particular, adopting analysis corresponding to different time intervals, we find that users lean towards old POIs in the short term and look for new POIs with the increase of the time interval. However, existing approaches usually neglect users’ conservatism and curiosity preferences. Therefore, they are confronted with a bottleneck of depicting accurate user needs, making it difficult to improve the recommendation performance further. Besides, we further find that the number of user daily check-ins has uneven distribution, which is not conducive to capture the accurate transition patterns of user behaviors. In light of the above, we design a single POI sequential method. On this basis, we propose a recommendation method of the variable additive Markov chain. We consider the user sequential preferences, especially liking old and pursuing new features. In addition, our model exploits the geographical tendency of user behaviors. Finally, we conduct abundant experiments on four cities in the two real datasets, i.e., Foursquare and Jiepang. The experimental results show its superiority over other competitors.

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Section: Related Workmentioning

confidence: 99%

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

2021

Wireless Communications and Mobile Computing

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“…As mentioned in [9], it presents effective solutions to the constraints. Besides users can comment after visiting and from these comments other users may get help.…”

Section: Literature Reviewmentioning

confidence: 99%

An Innovative Tour Recommendation System using Graph Algorithms

Hossain¹,

Tanim²,

Nawal³

et al. 2019

JISEBI

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Background: Tour recommendation and path planning are the most challenging jobs for tourists as they decide Points of Interest (POI).Objective: To reduce the physical effort of the tourists and recommend them a personalized tour is the main objective of this paper. Most of the time people had to find the places he wants to visit in a difficult way. It kills a lot of time.Methods: To cope with this situation we have used different methodology. First, a greedy algorithm is used for filtering the POIs and BFS (Breadth First Search) algorithm will find POI in terms of user interest. The maximum number of visited POI within a limited time will be considered. Then, the Dijkstra algorithm finds the shortest path from the point of departure to the end of tours.Results: This work shows its users list of places according to the user's interest in a particular city. It also suggests them places to visit in a range from the location of the user where a user can dynamically change this range and it also suggests nearby places they may want to visit.Conclusion: This tour recommendation system provides its users with a better trip planning and thus makes their holidays enjoyable.

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“…Therefore, trip planning should prepare an itinerary considering worst-case scenarios to satisfy constraints. In [26], Zhang et al studied tour recommendation considering POI availability and uncertain traveling times represented as random variables following some probability distributions. This approach is similar to our proposed model, but our probabilistic itinerary evaluation model expands modeling capabilities by including various uncertain factors, such as weather conditions, visit duration, and costs.…”

Section: Copyright Cmentioning

confidence: 99%

Personalized Trip Planning Considering User Preferences and Environmental Variables with Uncertainty

Kim

Hong

Suh

2019

IEICE Trans. Inf. & Syst.

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Personalized trip planning is a challenging problem given that places of interest should be selected according to user preferences and sequentially arranged while satisfying various constraints. In this study, we aimed to model various uncertain aspects that should be considered during trip planning and efficiently generate personalized plans that maximize user satisfaction based on preferences and constraints. Specifically, we propose a probabilistic itinerary evaluation model based on a hybrid temporal Bayesian network that determines suitable itineraries considering preferences, constraints, and uncertain environmental variables. The model retrieves the sum of time-weighted user satisfaction, and ant colony optimization generates the trip plan that maximizes the objective function. First, the optimization algorithm generates candidate itineraries and evaluates them using the proposed model. Then, we improve candidate itineraries based on the evaluation results of previous itineraries. To validate the proposed trip planning approach, we conducted an extensive user study by asking participants to choose their preferred trip plans from options created by a human planner and our approach. The results show that our approach provides human-like trip plans, as participants selected our generated plans in 57% of the pairs. We also evaluated the efficiency of the employed ant colony optimization algorithm for trip planning by performance comparisons with other optimization methods.

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Trip Recommendation Meets Real-World Constraints

Cited by 32 publications

References 39 publications

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

Joint Modeling of User Behaviors Based on Variable‐Order Additive Markov Chain for POI Recommendation

An Innovative Tour Recommendation System using Graph Algorithms

Personalized Trip Planning Considering User Preferences and Environmental Variables with Uncertainty

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