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Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency

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Abstract

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select points of interest (POI) to visit in unfamiliar cities and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photographs. Our tour recommendation problem is modeled using a formulation of the Orienteering problem and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations and demonstrate how POI visit duration can be personalized using this time-based user interest. Furthermore, we demonstrate how PersTour can be further enhanced by: (i) a weighted updating of user interests based on the recency of their POI visits and (ii) an automatic weighting between POI popularity and user interests based on the tourist’s activity level. Using a Flickr dataset of ten cities, our experiments show the effectiveness of PersTour against various collaborative filtering and greedy-based baselines, in terms of tour popularity, interest, recall, precision and F\(_1\)-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

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Notes

  1. We use the terms “tourist” and “user” interchangeably, and similarly for the terms “tour” and “trip.”

  2. This publication is an extended version of Lim et al. [27] that appeared in IJCAI’15, with the additional contributions of Points 3, 5 and 7.

  3. \(T^{\textit{Travel}}(p_x, p_y)\) can be easily generalized to different transport modes (e.g., taxi, bus, train) and to also consider the traffic condition between POIs (e.g., longer travel times between two POIs in a congested city, compared to two equal-distanced POIs elsewhere).

  4. Although we examine POIs in this work, our tour recommendation problem definition can be easily modified such that a recommended tour itinerary starts and ends at a specific hotel where the tourist is staying at.

  5. Some metrics are rounded off to the same value, but are different values before rounding. The bold-faced values indicate the best performing metrics.

  6. We can only compare POI visit durations for POIs in itinerary I that were “correctly” recommended (i.e., visited in real life).

  7. PT-.5T out-performs PT-.5U in only one city, performs the same in five cities and under-performs in the remaining four cities.

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Acknowledgements

This work was supported in part by Data61. We thank the anonymous reviewers for their useful comments and suggestions.

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Authors and Affiliations

  1. Department of Computing and Information Systems, University of Melbourne, Parkville, Australia

    Kwan Hui Lim, Jeffrey Chan, Christopher Leckie & Shanika Karunasekera

  2. Data61, CSIRO, Canberra, Australia

    Kwan Hui Lim

  3. School of Science, RMIT University, Melbourne, Australia

    Jeffrey Chan

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  1. Kwan Hui Lim
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Correspondence to Kwan Hui Lim.

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Lim, K.H., Chan, J., Leckie, C. et al. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency. Knowl Inf Syst 54, 375–406 (2018). https://doi.org/10.1007/s10115-017-1056-y

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