St-Pil

Cui, Qiang; Zhang, Chenrui; Zhang, Yafeng; Wang, Jinpeng; Cai, Mingchen

doi:10.1145/3459637.3482189

Cited by 29 publications

(1 citation statement)

References 9 publications

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“…As self-attention networks (SAN) show great potential in process sequential tasks, SAN-based models such as SASRec (Kang and McAuley, 2018 ) and TiSASRec (Li et al, 2020 ), quickly surpassing the traditional convolutional neural network (CNN) or RNN-based methods and becoming an advanced model in the field of sequential recommendation. Recently, some SAN-based works have further improved the performance of next Point-of-Interest (POI) proposals by introducing hierarchical grids (Lian et al, 2020 ; Cui et al, 2021 ). This innovative approach aims to fully exploit geographic information while taking into account non-adjacent locations and non-contiguous visits, improving model performance by explicitly incorporating spatial and temporal proximity.…”

Section: Introductionmentioning

confidence: 99%

Multi-granularity contrastive learning model for next POI recommendation

Zhu,

Yao,

Sun

2024

Front. Neurorobot.

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Next Point-of-Interest (POI) recommendation aims to predict the next POI for users from their historical activities. Existing methods typically rely on location-level POI check-in trajectories to explore user sequential transition patterns, which suffer from the severe check-in data sparsity issue. However, taking into account region-level and category-level POI sequences can help address this issue. Moreover, collaborative information between different granularities of POI sequences is not well utilized, which can facilitate mutual enhancement and benefit to augment user preference learning. To address these challenges, we propose multi-granularity contrastive learning (MGCL) for next POI recommendation, which utilizes multi-granularity representation and contrastive learning to improve the next POI recommendation performance. Specifically, location-level POI graph, category-level, and region-level sequences are first constructed. Then, we use graph convolutional networks on POI graph to extract cross-user sequential transition patterns. Furthermore, self-attention networks are used to learn individual user sequential transition patterns for each granularity level. To capture the collaborative signals between multi-granularity, we apply the contrastive learning approach. Finally, we jointly train the recommendation and contrastive learning tasks. Extensive experiments demonstrate that MGCL is more effective than state-of-the-art methods.

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