很多人关注了地理影响、社交关系、时空模式、文本内容信息,但却没关注图片本身包含的信息,尽管这已经被证明是很有用的。
【论文阅读】DisenPOI: Disentangling sequential and geographical influence for point-of-interest recommendation ---- 前言 2023 年,WSDM 的一篇论文:DisenPOI: Disentangling sequential and geographical influence for point-of-interest 参考资料 [1] DisenPOI: Disentangling sequential and geographical influence for point-of-interest recommendation
【论文阅读】Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation Metadata ---- 前言 刚出炉的IJCAI 2022的一篇论文:Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest 参考资料 [1] Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
【论文阅读】Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences Metadata authors ---- 前言 2022 年 IJCAI 的一篇论文,POI 推荐:Next Point-of-Interest Recommendation with Inferring Multi-step Future 参考资料 [1] Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences
【论文阅读】Next point-of-interest recommendation with auto-correlation enhanced multi-modal transformer network ⭐⭐⭐⭐ share:: false comment:: 框架为 Transformer,计算序列自相关性,并考虑访问子序列,同时预测 POI 及其类别 前言 2022,SIGIR: Next point-of-interest 参考资料 [1] Next point-of-interest recommendation with auto-correlation enhanced multi-modal transformer
【论文阅读】ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation Metadata 分别学习长期和短期的用户行为模式,并通过 Attention 融合 前言 CIKM,2021:ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest 参考资料 [1] ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation
鉴于[1] [2]已对推荐系统相关论文进行梳理,本文选择POI (Point-of-Interest)方向的论文进行解读。如果对该方向不了解,可以参考综述文章[3] [4]。 2.5 【short paper】Next Point-of-Interest Recommendation with Auto-Correlation Enhanced Multi-Modal Transformer 参考文献 [1] SIGIR 2022 | 推荐系统相关论文分类整理 [2] SIGIR 2022 推荐系统论文整理分类 [3] A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations [4] Point-of-Interest Recommender Systems based on Location-Based Social Networks
Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation Xiaolin Wang Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences Lu Zhang, Zhu Sun,
Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation 6 POI RS Next Point-of-Interest
---- 前言 随着基于位置的社交网络(Location-Based Social Network)的快速发展, 海量的签到数据被用于挖掘用户的行为模式以实现兴趣点(Point-of-Interest) 一般的,我们的任务是预测用户的下一个访问地点,即_next POI(Point-of-Interest) recommendation_。
Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization.
Problem【作为最大流量问题的校准化推荐】 【北大,美团】DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest
【IJCAI 2022】Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation 【IJCAI 2022】Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences 论文链接:未公开
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework, CIKM2023 7. CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework, CIKM2023 Ali Tourani, Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo https://arxiv.org/abs/2306.11395 Point-of-Interest (POI
Recommender System via Continuous-Time Modeling ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest
普渡大学的研究人员发表了一篇论文“Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction”,描述了使用
arxiv.org/abs/1903.03911 www.kevinkaixu.net/shape2motion.html Multiview 2D/3D Rigid Registration via a Point-Of-Interest We propose to tackle the problem of multiview 2D/3D rigid registration for intervention via a Point-Of-Interest
论文标题:Large Language Models for Next Point-of-Interest Recommendation 作者:Peibo Li(李沛博) ; Maarten de Rijke 2种POI推荐范式 A: 这篇论文试图解决的问题是如何有效地利用位置基社交网络(Location-based Social Network, LBSN)数据中的丰富上下文信息来提高下一个兴趣点(Point-of-Interest
A Study of Privacy Risk for Point-of-Interest Recommendation ROTAN: A Rotation-based Temporal Attention A Study of Privacy Risk for Point-of-Interest Recommendation ACM链接:https://dl.acm.org/doi/abs/10.1145
他的行动轨迹为 图片 ,其中 图片 ,表示用户uuu在tit_iti时刻到达位置为pip_ipi的地点viv_ivi,其中 图片 一般的,我们的任务是预测用户的下一个访问地点,即next POI(Point-of-Interest