Relation Embedding for Personalised POI Recommendation

• URL: http://arxiv.org/abs/2002.03461v2
• Date: Wed, 19 Feb 2020 16:40:48 GMT
• Title: Relation Embedding for Personalised POI Recommendation
• Authors: Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz
• Abstract summary: 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.
• Score: 34.043989803855844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.

Related papers

• Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization [75.1240295759264]
  We propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC. We increase the consistency and informativeness of the pairwise preference signals through targeted modifications. We identify that DPO alone is insufficient to model these correlations and capture nuanced variations.
  arXiv  Detail & Related papers  (2024-08-14T11:29:47Z)
• When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective [64.73162159837956]
  evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. We propose DataCOPE, a data-centric framework for evaluating a target policy given a dataset. Our empirical analysis of DataCOPE in the logged contextual bandit settings using healthcare datasets confirms its ability to evaluate both machine-learning and human expert policies.
  arXiv  Detail & Related papers  (2023-11-23T17:13:37Z)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• 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)
• Empowering Next POI Recommendation with Multi-Relational Modeling [27.27372864066066]
  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.
  arXiv  Detail & Related papers  (2022-04-24T21:51:29Z)
• A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation [6.346772579930929]
  We propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.
  arXiv  Detail & Related papers  (2022-01-20T12:41:12Z)
• Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation [2.896192909215469]
  We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets.
  arXiv  Detail & Related papers  (2022-01-10T16:46:27Z)

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.