Empowering Next POI Recommendation with Multi-Relational Modeling

• URL: http://arxiv.org/abs/2204.12288v1
• Date: Sun, 24 Apr 2022 21:51:29 GMT
• Title: Empowering Next POI Recommendation with Multi-Relational Modeling
• Authors: Zheng Huang, Jing Ma, Yushun Dong, Natasha Zhang Foutz, Jundong Li
• Abstract summary: Location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs. We propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module.
• Score: 27.27372864066066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the wide adoption of mobile devices and web applications, location-based social networks (LBSNs) offer large-scale individual-level location-related activities and experiences. Next point-of-interest (POI) recommendation is one of the most important tasks in LBSNs, aiming to make personalized recommendations of next suitable locations to users by discovering preferences from users' historical activities. Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations). Such relational information holds great potential to facilitate the next POI recommendation. However, most existing methods either focus on merely the user-POI visits, or handle different relations based on over-simplified assumptions while neglecting relational heterogeneities. To fill these critical voids, we propose a novel framework, MEMO, which effectively utilizes the heterogeneous relations with a multi-network representation learning module, and explicitly incorporates the inter-temporal user-POI mutual influence with the coupled recurrent neural networks. Extensive experiments on real-world LBSN data validate the superiority of our framework over the state-of-the-art next POI recommendation methods.

Related papers

• BiVRec: Bidirectional View-based Multimodal Sequential Recommendation [55.87443627659778]
  We propose an innovative framework, BivRec, that jointly trains the recommendation tasks in both ID and multimodal views. BivRec achieves state-of-the-art performance on five datasets and showcases various practical advantages.
  arXiv  Detail & Related papers  (2024-02-27T09:10:41Z)
• Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation [81.4999547454189]
  Existing works assume the available POI check-ins reported by users are the ground-truth depiction of user behaviors. In real application scenarios, the check-in data can be rather unreliable due to both subjective and objective causes. We propose a Bayes-enhanced Multi-view Attention Network to address the uncertainty factors of the user check-ins.
  arXiv  Detail & Related papers  (2023-11-01T12:47:38Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
  We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
  arXiv  Detail & Related papers  (2022-09-12T09:19:22Z)
• Decentralized Collaborative Learning Framework for Next POI Recommendation [39.65626819903099]
  Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. We propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.
  arXiv  Detail & Related papers  (2022-03-30T11:00:11Z)
• Knowledge-aware Coupled Graph Neural Network for Social Recommendation [29.648300580880683]
  We propose a Knowledge-aware Coupled Graph Neural Network (KCGN) that injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. We further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns.
  arXiv  Detail & Related papers  (2021-10-08T09:13:51Z)
• Dual Side Deep Context-aware Modulation for Social Recommendation [50.59008227281762]
  We propose a novel graph neural network to model the social relation and collaborative relation. On top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
  arXiv  Detail & Related papers  (2021-03-16T11:08:30Z)
• STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation [22.705788963791445]
  Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. We propose a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference neighbourhoods.
  arXiv  Detail & Related papers  (2020-10-06T04:03:42Z)
• Reward Constrained Interactive Recommendation with Natural Language Feedback [158.8095688415973]
  We propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference. Our proposed framework is general and is further extended to the task of constrained text generation.
  arXiv  Detail & Related papers  (2020-05-04T16:23:34Z)
• Relation Embedding for Personalised POI Recommendation [34.043989803855844]
  We propose a translation-based embedding for POI recommendation. Our approach encodes the temporal and semantic contents effectively in a low-temporal relation space. A combined factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests.
  arXiv  Detail & Related papers  (2020-02-09T22:26:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.