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Attentive multi-task learning for group itinerary recommendation

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Abstract

Tourism is one of the largest service industries and a popular leisure activity participated by people with friends or family. A significant problem faced by the tourists is how to plan sequences of points of interest (POIs) that maintain a balance between the group preferences and the given temporal and spatial constraints. Most traditional group itinerary recommendation methods adopt predefined preference aggregate strategies without considering the group members’ distinctive characteristics and inner relations. Besides, POI textual information is beneficial to capture overall group preferences but is rarely considered. With these concerns in mind, this paper proposes an AMT-IRE (short for Attentive Multi-Task learning-based group Itinerary REcommendation) framework, which can dynamically learn the inner relations between group members and obtain consensus group preferences via the attention mechanism. Meanwhile, AMT-IRE integrates POI categories and POI textual information via another attention network. Finally, the group preferences are used in a variant of the orienteering problem to recommend group itineraries. Extensive experiments on six datasets validate the effectiveness of AMT-IRE.

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Notes

  1. Visit duration means the duration time spent at a POI; visit order is the order in which POIs are visited; itinerary creation refers to whether to create an itinerary that includes the origin and destination; user profile can include age, occupation, and hobbies; predefined group means whether the group members are predefined; preference aggregation is a way of obtaining group preferences by fusing group member preferences; and dynamic weight refers to whether the preference aggregation method is dynamic. For a more detailed description of the influential factors, we refer the interested reader to [30].

  2. https://www.wikipedia.org/.

  3. https://pytorch.org/.

  4. User-77 has visited POI-46 Caffè Giubbe Rosse, POI-78 Caffè Concerto Paszkowski, and POI-95 Rivoire Coffee; User-193 has visited POI-54 Piazza della Signoria, POI-135 Loggia della Signoria, POI-198 Via de’ Benci, and POI-168 Fontana del Nettuno; and User-575 has visited POI-194 Palazzo Gherardi Uguccioni, POI-198 Via de’ Benci, and POI-247 Via degli Speziali.

  5. Group-107 has visited POI-64 Pantheon, POI-143 Domus Aurea, and POI-28 Temple of Vespasian and Titus; Group-116 has visited POI-58 Palatine Hill, POI-109 Fontana dei Quattro Fiumi, POI-135 Colosseum, and POI-91 Rebibbia Station; and Group-153 has visited POI-20 Stadio Olimpico and POI-46 Ponte Sant’Angelo.

  6. User-134 has visited POI-35 Vatican Museums, POI-177 Castel Sant’Angelo, and POI-192 Galleria Alberto Sordi; User-218 has visited POI-192 Galleria Alberto Sordi, POI-268 Musei Capitolini, and POI-276 Galleria Doria Pamphilj; User-67 has visited POI-135 Colosseum, POI-34 Bibliotheca Hertziana, POI-228 Obelisco del Pantheon, and POI-55 Vatican Library; and User-373 has visited POI-178 Piazza Venezia, POI-228 Obelisco del Pantheon, and POI-192 Galleria Alberto Sordi.

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Acknowledgements

This research is partially supported by the National Key Research and Development Program of China under Grant 2017YFD0401001, in part by the Key Program of National Natural Science Foundation of China under Grant 92046026, in part by the National Natural Science Foundation of China under Grant 71701089, in part by the Jiangsu Provincial Key Research and Development Program, China under Grant BE2020001-3, and in part by the jiangsu Provincial Policy Guidance Program, China under Grant BZ2020008.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Lei Chen, Jie Cao, Weichao Liang & Haicheng Tao

  2. School of Information Engineering, Nanjing University of Finance and Economics, Nanjing, China

    Jie Cao & Guixiang Zhu

  3. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Huanhuan Chen

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  1. Lei Chen
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Correspondence to Jie Cao.

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Chen, L., Cao, J., Chen, H. et al. Attentive multi-task learning for group itinerary recommendation. Knowl Inf Syst 63, 1687–1716 (2021). https://doi.org/10.1007/s10115-021-01567-3

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