Personalized Location Recommendations with Local Feature Awareness

Zhu, Xiaoyan; Hao, Ripei; Chi, Haotian; Du, Xiaojiang

doi:10.1109/glocom.2016.7842140

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…The development of location-based social networks (LBSNs) has produced a wealth of historical information related to users, such as geographical locations and user preferences. Many studies [9,13,[26][27][28] have utilized the check-in data generated by social tools, such as Foursquare, Flicker, and Facebook, or photo data with geo-tagged information, as a scoring basis to generate personalized tour routes based on user preferences. However, when utilizing a simple popularity score as the basis for recommendations, the produced routes cannot adequately satisfy the personal interests of users.…”

Section: Poi Scoring Mechanismmentioning

confidence: 99%

A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

et al. 2021

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Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time.

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