Landmark-Based Route Recommendation with Crowd Intelligence

Zheng, Bolong; Su, Han; Zheng, Kai; Zhou, Xiaofang

doi:10.1007/s41019-016-0013-1

Cited by 10 publications

(3 citation statements)

References 25 publications

(33 reference statements)

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“…This paper uses the orientation problem to return an optimal travel route for the user, considering the interest preference of users and the popularity of POI. The personalized recommendation method proposed in this paper incorporates two social factors: preferences of user in travelogue graphic information and interpersonal interest similarity [27]. Therefore, we first introduce the user interest factors.…”

Section: Basic Definitionmentioning

confidence: 99%

Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

Lü¹,

Zhang

2022

Wireless Communications and Mobile Computing

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On the way to travel, the public expect to get a tourism experience with low cost, convenient travel, and high comfort. At the same time, they also have different tourism needs such as history and culture, natural landscape, and food shopping. To address the problem that traditional travel route recommendation algorithms have limited accuracy and only analyze text or pictures alone, we propose a personalized travel route recommendation algorithm that integrates text and photo information from travelogues and obtain the historical tourism footprint of tourists by analyzing travel notes. According to the frequency and cooccurrence of scenic spots in the travel notes and the number of photos taken by each scenic spot, the popularity of scenic spots and the interest preferences of various types of tourists are analyzed. Under the given starting and ending points or passing points, the optimal tourism route generation method is designed. Experiments on the real data set of Ctrip Travel website show that the recommendation accuracy of this algorithm is significantly improved compared with the traditional algorithm which only uses travel notes text or photos. Compared with the algorithm that only considers the popularity of interest points or tourists’ interest preferences, the accuracy of the route recommended by the algorithm is improved. Compared with the algorithm that only considers the cooccurrence of scenic spots or only considers the influence of photos, this algorithm can obtain a better popularity score of scenic spots. This method integrates the two kinds of information including picture and text, fully considering the interest of users with high practicability.

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Section: Basic Definitionmentioning

confidence: 99%

Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

Lü¹,

Zhang

2022

Wireless Communications and Mobile Computing

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“…The evaluation results showed that the proposed system can recommend the best route based on user feedbacks. Zheng et al [146] extended the CrowdPlanner system by proposing some strategies to verify truths and know the best routes near the locations and dealing with text queries more efficiently. Besides, it evaluated route trip by mapping services with popular route-mining algorithms.…”

Section: ) Travel Route Recommendationsmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. 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. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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“…Generally, there are two different types of memory-based CF approaches: user based [30,32] and item based [3]. The user-based CF model can be used in POI recommendation by calculating the the probability of visiting all pairwise POIs 4 [40,41] for estimating the probability that a user checks in a new POI. Different from using user-item rating records in classical matrix factorization approaches, another model [20] is based on check-in frequency that quantifies users' preference on locations.…”

Section: Collaborativementioning

confidence: 99%

Group-Based Recurrent Neural Networks for POI Recommendation

Zheng

et al. 2020

ACM/IMS Trans. Data Sci.

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With the development of mobile Internet, many location-based services have accumulated a large amount of data that can be used for point-of-interest (POI) recommendation. However, there are still challenges in developing an unified framework to incorporate multiple factors associated with both POIs and users due to the heterogeneity and implicity of this information. To alleviate the problem, this work proposes a novel group-based method for POI recommendation jointly considering the reviews, categories, and geographical locations, called the Group-based Temporal Sentiment-Aspect-Region Recurrent Neural Network (GTSAR-RNN). We divide the users into different groups and then train an individual RNN for each group with the goal of improving its pertinence. In GTSAR-RNN, we consider not only the effects of temporal and geographical contexts but also the users' sentimental opinions on locations. Experimental results show that GTSAR-RNN acquires significant improvements over the baseline methods on real datasets.

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Landmark-Based Route Recommendation with Crowd Intelligence

Cited by 10 publications

References 25 publications

Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Group-Based Recurrent Neural Networks for POI Recommendation

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