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GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendation

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Abstract

Trip recommendation, which aims to recommend a sequence of point-of-interests (POIs) as a trip to visit, is of great importance to location-based service systems. However, due to data sparsity, existing methods lack the ability to capture complex POI correlations in trips, resulting in limited recommendation performance. In this paper, we propose a graph contextualized trip recommendation model GC-TripRec with adversarial learning to take advantage of graph-based representation learning, so that enhanced trip recommendation can be supported by capturing essential knowledge about complex POI correlations in trips. Specifically, it first models pairwise transition relationships by a global transition graph, upon which global-level POI embedding can be captured by a graph convolution network. We further generate trajectory contextualized POI representation via a trip-level embedding method that compromises transition-based and category-based information of POIs in a single trip. In addition, trip representation is learned through Long Short-Term Memory (LSTM) with an adversarial learning task, such that query representation space would be highly overlapped with trip representation space, enabling getting an excellent trip recommendation system. Comprehensive experiments on four real datasets demonstrate that our proposed GC-TripRec significantly outperforms state-of-the-art methods.

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Availability of data and materials

Four different datasets, Edinburgh, Glasgow, Osaka and Toronto: public datasets available at paper: Learning Points and Routes to Recommend Trajectories.

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Funding

The funding concludes the National Natural Science Foundation of China (No. 62102277), Natural Science Foundation of Jiangsu Province (BK20210703).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    **yi Zhao, Junhua Fang, **fu Chao & Ruoqian Zhang

  2. Dalian Maritime University, Dalian, China

    Bo Ning

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  1. **yi Zhao
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  2. Junhua Fang
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  3. **fu Chao
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  5. Ruoqian Zhang
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Contributions

**yi Zhao wrote the main manuscript text. Junhua Fang, **fu Chao, Bo Ning and Ruoqian Zhang participated in model design and technical discussion.

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Correspondence to Junhua Fang, **fu Chao, Bo Ning or Ruoqian Zhang.

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This article belongs to the Topical Collection: Special Issue on Spatiotemporal Data Management and Analytics for Recommender Systems Guest Editors: Shuo Shang, **angliang Zhang and Panos Kalnis

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Zhao, J., Fang, J., Chao, P. et al. GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendation. World Wide Web 26, 2291–2310 (2023). https://doi.org/10.1007/s11280-022-01127-x

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