Exploiting large-scale check-in data to recommend time-sensitive routes

Hsieh, Hsun-Ping; Li, Cheng-Te; Lin, Shou-De

doi:10.1145/2346496.2346506

Cited by 70 publications

(19 citation statements)

References 18 publications

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“…Hsieh at el. [7] proposed a time-sensitive model based on the information squeezing from Gowalla check-in data and used greedy algorithm to recommend trip routes. In [6], Hsieh at el.…”

Section: Trip Recommendationmentioning

confidence: 99%

Trip Recommendation with Multiple User Constraints by Integrating Point-of-Interests and Travel Packages

Fang

Tseng

2014

2014 IEEE 15th International Conference on Mobile Data Management

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With the advances of mobile communication techniques in recent years, numerous kinds of Location-Based Services (LBSs) have been developed and one popular application of LBSs is trip recommendation. Although there exist already a number of studies on this topic in literatures, most of them focused on combining a set of point-of-interests (POIs, or say attractions) as a trip based on user-specific constraints. In another way, some few works discussed making recommendation in terms of travel packages, which have the benefits of lower cost and higher convenience. However, no prior work explores to integrate attractions and travel packages simultaneously for trip recommendation. In fact, such a hybrid-style recommender can provide higher benefits for users although there exist critical challenges here like the efficiency issue in such kind of real-time applications. In this paper, we propose a novel framework named Package-Attraction-based Trip Recommender (PATR) to efficiently recommend the personalized trips satisfying multiple constraints by effectively combining attractions and packages. In PATR, a Score Inference Model is proposed to infer the scores of attractions and packages by taking user-based preference and temporal-based properties into account. Then, the Hybrid Trip-Mine algorithm is proposed to efficiently discover the optimal trip which satisfies the multiple user-specific constraints with both of attractions and packages considered simultaneously. Furthermore, we propose two pruning strategies based on Hybrid Trip-Mine, named Score Estimation (SE) and Score Bound Tightening (SBT), to further improve the execution efficiency and memory utilization. To the best of our knowledge, this is the first work on travel recommendation that considers attractions and packages simultaneously. Through extensive experimental evaluations, our proposed approaches were shown to deliver excellent performance.

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“…Hsieh at el. [7] proposed a time-sensitive model based on the information squeezing from Gowalla check-in data and used greedy algorithm to recommend trip routes. In [6], Hsieh at el.…”

Section: Trip Recommendationmentioning

confidence: 99%

Trip Recommendation with Multiple User Constraints by Integrating Point-of-Interests and Travel Packages

Fang

Tseng

2014

2014 IEEE 15th International Conference on Mobile Data Management

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“…For this, the method extracted crowd's movements using geo-tagged tweets collected from Twitter and constructed a crowd footprint networks. Hsieh et al proposed a method to recommend a time-sensitive route when given a location with the starting time by exploiting crowd-sourced check-in data [5]. This method computes a route based on four factors; the popularity of places, the visiting order of places, the proper time to visit each place, and the proper transit time duration form one place to another.…”

Section: Figure 4: Estimating the Occurrence Of Tweets In Each Intersmentioning

confidence: 99%

Crowd-sourced prediction of pedestrian congestion for bike navigation systems

Wakamiya

Kawai

Kawasaki

et al. 2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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GPS-based navigation systems widely available on automobiles and smartphones nowadays are essential to find the best routes in the complicated urban space. However, it is still difficult for bikers to take full advantages of such navigation systems due to the lack of consideration on the different driving conditions. Generally, motorcyclists and cyclists take rides on narrow alleys and sidewalks which have a high risk of bumping against pedestrians. Therefore, it is necessary to find comfortable driving routes, also possibly avoiding areas congested by crowds. However, it is impractical to monitor crowd's existence everywhere at all times for such crowd-aware navigation. To overcome this limitation, we attempt to utilize location-based social network services where geo-tagged microblogs from massive crowd can be a good alternative source to measure pedestrian congestion in urban areas. In this paper, we introduce a route search method for bikers particularly to exploit crowd's volunteering reports being streamed via microblogs. In order to estimate human traffic from microblogs, we develop a crowd flow network which captures probable crowd movement on an urban network. We also examine the possible intersections which are expected to be highly congested based on the model. On the crowd flow network, we will find the best routes consisting of comfortable intersections and streets for the bike navigation systems.

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“…Many existing route recommendation systems suggest routes directly based on the similarity between user's visiting history in other contexts and other people's trip records in the targeted city [39]. Others identify venues according to a user's preference and recommend routes based on certain criteria (e.g., with the highest route score) [12], [15], [19], [25]. However, during the actual trip planning, it is common that a user may have some additional constraints such as "need to go some specific places," "go to park before lunch," and "the total travel time should be ≤ 6 h." Some prior work asked users to manually select and configure travel routes after recommendation, which was tedious and time consuming [19], [34].…”

Section: Introductionmentioning

confidence: 99%

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

Chen

Zhang

Guo

et al. 2015

IEEE Trans. Intell. Transport. Syst.

118

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Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TRIPPLANNER, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TRIPPLANNER works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TRIPPLANNER applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

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Exploiting large-scale check-in data to recommend time-sensitive routes

Cited by 70 publications

References 18 publications

Trip Recommendation with Multiple User Constraints by Integrating Point-of-Interests and Travel Packages

Trip Recommendation with Multiple User Constraints by Integrating Point-of-Interests and Travel Packages

Crowd-sourced prediction of pedestrian congestion for bike navigation systems

TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints

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