A self-adaptive point-of-interest recommendation algorithm based on a multi-order Markov model

Liu, Shudong; Wang, Lei

doi:10.1016/j.future.2018.07.008

Cited by 30 publications

(12 citation statements)

References 36 publications

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“…Liu and Wang [110] studied the problems of the prediction in the next point of interest recommendations by considering the current location and previous location. They applied the Markov model to combine the geographical influence and temporal popularity of users' checked-ins in the recommendation algorithm.…”

Section: ) Stand-alone Point Location 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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Section: ) Stand-alone Point Location 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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“…Based on this, reference [12] divides the working days and rest days based on time, and reference [13] divides the user's check-in time into segments, and divides the 24 hours of the day into 24 time segments. Reference [14], the time series is considered to be continuous, and the Markov chain sequence is constructed to predict the location to be checked-in at the next time.…”

Section: Influence Based On Timementioning

confidence: 99%

“…When the user check-in, the closer the distance is, the higher the probability of signing in, and the checking-in conforms to the power law distribution [3]. The Power-Law distribution represents the probability relationship between two locations where users check-in, and is expressed by (14)as:…”

Section: B Geographical Factorsmentioning

confidence: 99%

A Point-of-Interest Recommendation Algorithm Combining Social Influence and Geographic Location Based on Belief Propagation

Feng

2020

IEEE Access

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The location-based social network (LBSN) contains a large amount of user check-in data informations, in order to better improve the recommendation performance and avoid the impact of user check-in data sparsity. It is proposed to mine the time-category informations in the user's check-in data to carry out network modeling; by using the belief propagation algorithm on the time-category Markov network to obtain the user's social influence set. Calculating the similarity and familiarity of the social users in the collection, linearly integrate the unified social influence factors and geographical location influences, to recommend locations. Experimental analysis in the Foursquare dataset, compared with other algorithms, the recommendation algorithm performance of combining social influence and geographic location based on belief propagation has improved.

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“…Lassoued et al [43] proposed a novel algorithm based on the formalism of HMM for route and destination prediction. An algorithm based on a multi-order Markov model was proposed by Liu et al [44] for recommending the POIs, which could predict the next POIs of users' favorites. Combining the Markov model and PPM (prediction by partial matching) technique, Neto et al [45] presented a novel predictor which could automatically predict the route and destination in real time.…”

Section: Related Workmentioning

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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A self-adaptive point-of-interest recommendation algorithm based on a multi-order Markov model

Cited by 30 publications

References 36 publications

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

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

A Point-of-Interest Recommendation Algorithm Combining Social Influence and Geographic Location Based on Belief Propagation

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

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