Personalized Itinerary Recommendation with Queuing Time Awareness

Lim, Kwan Hui; Chan, Jeffrey; Karunasekera, Shanika

doi:10.1145/3077136.3080778

Cited by 70 publications

(61 citation statements)

References 36 publications

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“…Zhang et al [27,28] studied tour recommendation with the goal of recommending personalized itineraries based on the interest preferences of users and available touring time, while considering opening hours of POIs and uncertainty in travelling time. Other studies consider more practical factors that raise novel optimization challenges incorporating forms of situational awareness such as multiple modes of transport [29], considering traffic conditions [30][31][32], POI crowdedness [33,34], and queuing times [35].…”

Section: Related Workmentioning

confidence: 99%

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Bin

Sun

et al. 2019

Wireless Communications and Mobile Computing

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Tourism recommendation systems play a vital role in providing useful travel information to tourists. However, existing systems rarely aim at recommending tangible itineraries for tourists within a specific POI due to their lack of onsite travel behavioral data and related route mining algorithms. To this end, a novel travel route recommendation system is proposed, which collects tourist onsite travel behavior data automatically regarding a specific POI based on smart phone and IoT technology. Then, the proposed system preprocesses the behavior data to transform raw behavior sequences into Tourist-Behavior pattern sequences. Subsequently, the system discovers frequent travel routes from the generated pattern sequences by using an original route mining algorithm, named Tourist-Behavior PrefixSpan. Finally, a route-recommending method is designed to search and rank tangible travel routes according to the querying tourist's profile and constraint. The experimental results demonstrate that the proposed system is efficient and effective in recommending POI-oriented tangible travel routes considering tourists' route constraints and personal profile while ensuring that the suggested routes have considerable route values.

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Section: Related Workmentioning

confidence: 99%

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Bin

Sun

et al. 2019

Wireless Communications and Mobile Computing

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“…After that, we add the keyword to the result set and assign the result set to pre set (lines [14][15][16][17]. After the initial step, the algorithm finds the service with lower priority through the last finished service and stores the service into the result set (lines [19][20][21][22][23][24][25][26][27][28]. Finally, the algorithm outputs the result set (line 29).…”

Section: Priority Querymentioning

confidence: 99%

“…Even with 150 POIs to choose from, the number of possible routes consisting of 5 POIs can reach 70 billion. Compared to our work, Jeffrey [28] evaluated its itinerary recommendation methods using theme park data, where each park contains only 20 to 30 attractions.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Semantic‐Aware Top‐k Multirequest Optimal Route

et al. 2019

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In recent years, research on location-based services has received a lot of interest, in both industry and academic aspects, due to a wide range of potential applications. Among them, one of the active topic areas is the route planning on a point-of-interest (POI) network. We study the top-k optimal routes querying on large, general graphs where the edge weights may not satisfy the triangle inequality. The query strives to find the top-k optimal routes from a given source, which must visit a number of vertices with all the services that the user needs. Existing POI query methods mainly focus on the textual similarities and ignore the semantic understanding of keywords in spatial objects and queries. To address this problem, this paper studies the semantic similarity of POI keyword searching in the route. Another problem is that most of the previous studies consider that a POI belongs to a category, and they do not consider that a POI may provide various kinds of services even in the same category. So, we propose a novel top-k optimal route planning algorithm based on semantic perception (KOR-SP). In KOR-SP, we define a dominance relationship between two partially explored routes which leads to a smaller searching space and consider the semantic similarity of keywords and the number of single POI’s services. We use an efficient label indexing technique for the shortest path queries to further improve efficiency. Finally, we perform an extensive experimental evaluation on multiple real-world graphs to demonstrate that the proposed methods deliver excellent performance.

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“…Recent works have shown the effectiveness of geotagged photos in improving the itinerary recommendation performance [2]- [5]. In particular, the main idea of these approaches is to learn a sequence of POIs and consider many factors such as user interest [6], POI popularity [8], POI category [9], and trip constraints like time [7] and cost [15] for constructing the itinerary planning models. However, most of these works are proposed based on the Orienteering Problem (OP) or traveling salesman problem (TSP) variants.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Yochum

Chang

et al. 2020

IEEE Access

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Traveling as a very popular leisure activity enjoyed by many people all over the world. Typically, tourists have different kinds of preferences about their itineraries, limited time budgets, unfamiliar with the wide range of Points-of-Interest (POIs) in a city, so that planning an itinerary is quite tedious, time-consuming, and challenging for them. In this paper, we propose an adaptive genetic algorithm for personalized itinerary planning for travelers to plan their itineraries better. Firstly, desired starting POIs (e.g., POIs that are close to their hotels) and destination POIs (e.g., POIs that are near train stations or airports) are considered in our approach. Secondly, we also take some general factors into account that travelers would consider in their preferences of an itinerary, which are mandatory POIs, the total number of POIs, the overall POI popularity, the overall cost, and the overall rating. Thirdly, we view this kind of recommendation task as a Multi-Objective Optimization problem, and we propose an adaptive genetic algorithm with the crossover and mutation probabilities (AGAM) for solving this problem to better find the best global solution. Fourthly, we allocate different weights to every factor which considered in our paper to generate a personalized itinerary recommendation for better meet many kinds of preferences of tourists. Finally, we compare our approach against baselines on real-world datasets which include six touristic cities, and the experimental results show that the AGAM achieves better recommendation performance in terms of the mandatory POIs, total POI visits, overall POI popularity, total travel time (including travel time and visit duration), overall cost, and overall rating.

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Personalized Itinerary Recommendation with Queuing Time Awareness

Cited by 70 publications

References 36 publications

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

A Travel Route Recommendation System Based on Smart Phones and IoT Environment

Semantic‐Aware Top‐k Multirequest Optimal Route

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

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