Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

Jian-feng, Huang; Liu, Yuefeng; Chen, Yue; Chen, Jia

doi:10.3390/ijgi8100433

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…Huang et al [122] proposed Dynamic Recommendation of POI Sequence (DRPS) which is based on DNN. This model consists of an encoder and a decoder module and for getting better performance, this model takes into account the POI embedding feature, the geographical and categorical influences of historical trajectories, and the positional encoding.…”

Section: Poi Sequence Recommendationmentioning

confidence: 99%

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Islam¹,

Mohammad²,

Das³

et al. 2020

Preprint

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Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.

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Section: Poi Sequence Recommendationmentioning

confidence: 99%

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Islam¹,

Mohammad²,

Das³

et al. 2020

Preprint

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“…To achieve successive POI recommendations, Lin et al [10] integrated users' preferences, the influence of time and the influence of geography, and used the MF to analyze the interaction among them. Huang et al [4] proposed a new framework named DRPS (dynamic recommendation of POI sequence), which is a dynamic recommendation framework for POI sequences. Ying et al [11] proposed a MEAP-T (time-aware metric embedding approach with asymmetric projection) method to solve the asymmetry of POI transitions for recommending the most possible successive POIs.…”

Section: Related Workmentioning

confidence: 99%

“…In previous studies, many achievements have been reached in successive POI recommendations and tourism route planning. It has fascinated scholars, starting with existing traditional approaches [1,2], and expressly proposing novel optimal route planning methods for moving objects [3,4]. However, important issues are still open.…”

Section: Introductionmentioning

confidence: 99%

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data

Zeng

Qin

et al. 2020

Sensors

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With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people’s travel routes under different spatiotemporal backgrounds but also is close to people’s natural selection by the perception of the group.

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“…us, the recommendations are not suitable for every visitor because of a lack of personalization [4]. When modelling and solving the tour route planning problem, most papers investigate user preference and give travel route recommendations with a fixed start and endpoints [5], not taking the spatiotemporal travel sequence, length of stay, and ways of transport into consideration [6,7].…”

Section: Introductionmentioning

confidence: 99%

Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity

et al. 2020

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Travel route preferences can strongly interact with the events that happened in networked traveling, and this coevolving phenomena are essential in providing theoretical foundations for travel route recommendation and predicting collective behaviour in social systems. While most literature puts the focus on route recommendation of individual scenic spots instead of city travel, we propose a novel approach named City Travel Route Recommendation based on Sequential Events Similarity (CTRR-SES) by applying the coevolving spreading dynamics of the city tour networks and mine the travel spatiotemporal patterns in the networks. First, we present the Event Sequence Similarity Measurement Method based on modelling tourists’ travel sequences. The method can help measure similarities in various city travel routes, which combine different scenic types, time slots, and relative locations. Second, by applying the user preference learning method based on scenic type, we learn from the user’s city travel historical data and compute the personalized travel preference. Finally, we verify our algorithm by collecting data of 54 city travellers of their historical spatiotemporal routes in the ten most popular cities from Mafeng.com. CTRR-SES shows better performance in predicting the user’s new city travel sequence fitting the user’s individual preference.

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Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

Cited by 13 publications

References 35 publications

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data

Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity

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