Learning Points and Routes to Recommend Trajectories

Chen, Fan; Ong, Cheng Soon; Xie, Lexing

doi:10.1145/2983323.2983672

Cited by 80 publications

(89 citation statements)

References 21 publications

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“…With the availability of user-generated trajectory information, route recommendation has received much attention from the research community [6,7,11], which aims to generate reachable paths between the source and destination locations. The task can be defined as either personalized [6,7] or nonpersonalized [4,11,17,35], and constructed based on different types of trajectory data, e.g., GPS data [35] or POI check-in data [3,22]. In the literature, various methods have been developed for route recommendation, including graph search algorithms [4,15,30], time-sensitive algorithms [17], A * search algorithm [11], probabilistic POI transition/ranking models [3] and diver-direction based methods [35].…”

Section: Related Workmentioning

confidence: 99%

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Wang

Zhao

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Personalized Route Recommendation (PRR) aims to generate user-specific route suggestions in response to users' route queries. Early studies cast the PRR task as a pathfinding problem on graphs, and adopt adapted search algorithms by integrating heuristic strategies. Although these methods are effective to some extent, they require setting the cost functions with heuristics. In addition, it is difficult to utilize useful context information in the search procedure. To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A * algorithm, for the PRR task. Our model consists of two components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use a value network for estimating the cost from a candidate location to the destination. For capturing structural characteristics, the value network is built on top of improved graph attention networks by incorporating the moving state of a user and other context information. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Wang

Zhao

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…A common approach for the orienteering problems is the integer linear programming (ILP) algorithm [3,14]. However, ILP does not apply directly to our problem.…”

Section: The C-ilp Algorithmmentioning

confidence: 99%

“…This is because the second term in our objective function in Equation 20, i.e., |V |−1 i=2 |V |−1 j=i+1 xi ·xj ·f ( − → eij ), is nonlinear. In what follows, we transform Equation 20 to a linear form such that the ILP algorithm [3,14] can be applied to solve our problem. Such an algorithm finds the exact optimal trip for TripRec.…”

Section: The C-ilp Algorithmmentioning

confidence: 99%

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A Joint Context-Aware Embedding for Trip Recommendations

Ramamohanarao

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Trip recommendation is an important location-based service that helps relieve users from the time and efforts for trip planning. It aims to recommend a sequence of places of interest (POIs) for a user to visit that maximizes the user's satisfaction. When adding a POI to a recommended trip, it is essential to understand the context of the recommendation, including the POI popularity, other POIs co-occurring in the trip, and the preferences of the user. These contextual factors are learned separately in existing studies, while in reality, they impact jointly on a user's choice of a POI to visit. In this study, we propose a POI embedding model to jointly learn the impact of these contextual factors. We call the learned POI embedding a contextaware POI embedding. To showcase the effectiveness of this embedding, we apply it to generate trip recommendations given a user and a time budget. We propose two trip recommendation algorithms based on our contextaware POI embedding. The first algorithm finds the exact optimal trip by transforming and solving the trip recommendation problem as an integer linear programming problem. To achieve a high computation efficiency, the second algorithm finds a heuristically optimal trip based on adaptive large neighborhood search. We perform extensive experiments on real datasets. The results show that our proposed algorithms consistently outperform state-of-the-art algorithms in trip recommendation quality, with an advantage of up to 43% in F1-score.

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“…As tourists travel from one location to another in the city, they make different transitions with different frequencies (weights); we introduce these as transition patterns in this paper. An idea similar to this paper is considered in [5]. Both location and transition knowledge are jointly modeled to recommend a travel route.…”

Section: Introductionmentioning

confidence: 99%

User Transition Pattern Analysis for Travel Route Recommendation

Sun

Zhuang

2019

IEICE Trans. Inf. & Syst.

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A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.

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Learning Points and Routes to Recommend Trajectories

Cited by 80 publications

References 21 publications

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

A Joint Context-Aware Embedding for Trip Recommendations

User Transition Pattern Analysis for Travel Route Recommendation

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