Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities

• URL: http://arxiv.org/abs/2308.09309v1
• Date: Fri, 18 Aug 2023 05:07:41 GMT
• Title: Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities
• Authors: Jinze Wang, Lu Zhang, Zhu Sun, Yew-Soon Ong
• Abstract summary: We propose a novel Meta-learning Enhanced next POI Recommendation framework (MERec) MERecleverages the correlation of check-in behaviors among various cities into the meta-learning paradigm to help infer user preference in the target city. In particular, a city-level correlation strategy is devised to attentively capture common patterns among cities, so as to transfer more relevant knowledge from more correlated cities.
• Score: 32.70591612636725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing point-of-interest (POI) recommenders aim to capture user preference by employing city-level user historical check-ins, thus facilitating users' exploration of the city. However, the scarcity of city-level user check-ins brings a significant challenge to user preference learning. Although prior studies attempt to mitigate this challenge by exploiting various context information, e.g., spatio-temporal information, they ignore to transfer the knowledge (i.e., common behavioral pattern) from other relevant cities (i.e., auxiliary cities). In this paper, we investigate the effect of knowledge distilled from auxiliary cities and thus propose a novel Meta-learning Enhanced next POI Recommendation framework (MERec). The MERec leverages the correlation of check-in behaviors among various cities into the meta-learning paradigm to help infer user preference in the target city, by holding the principle of "paying more attention to more correlated knowledge". Particularly, a city-level correlation strategy is devised to attentively capture common patterns among cities, so as to transfer more relevant knowledge from more correlated cities. Extensive experiments verify the superiority of the proposed MERec against state-of-the-art algorithms.

Related papers

• Urban traffic analysis and forecasting through shared Koopman eigenmodes [5.207485728774798]
  Predicting traffic flow in data-scarce cities is challenging due to limited historical data. We leverage transfer learning by identifying periodic patterns common to data-rich cities. This method uncovers common eigenmodes (urban heartbeats) in traffic patterns and transfers them to data-scarce cities.
  arXiv  Detail & Related papers  (2024-09-07T06:24:50Z)
• Urban Mobility Assessment Using LLMs [19.591156495742922]
  This work proposes an innovative AI-based approach for synthesizing travel surveys by prompting large language models (LLMs) Our study evaluates the effectiveness of this approach across various U.S. metropolitan areas by comparing the results against existing survey data at different levels.
  arXiv  Detail & Related papers  (2024-08-22T19:17:33Z)
• 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)
• Meta-Learning over Time for Destination Prediction Tasks [53.12827614887103]
  A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain. Recent studies have found, at best, only marginal improvements in predictive performance from incorporating temporal information. We propose an approach based on hypernetworks, in which a neural network learns to change its own weights in response to an input.
  arXiv  Detail & Related papers  (2022-06-29T17:58:12Z)
• Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach [66.31587753595291]
  We propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. For mobile users, the content popularity is predicted via user preference learning.
  arXiv  Detail & Related papers  (2022-06-13T03:34:00Z)
• A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling [24.103961649276584]
  We propose a novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the data insufficiency and privacy problems. CcFTL transfers the relational knowledge from multiple rich-data source cities to the target city. We take the urban region profiling as an application of smart cities and evaluate the proposed method with a real-world study.
  arXiv  Detail & Related papers  (2022-05-31T12:41:01Z)
• LearningCity: Knowledge Generation for Smart Cities [0.23624125155742054]
  We present LearningCity, our solution that has been validated over an existing smart city deployment in Santander. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.
  arXiv  Detail & Related papers  (2021-04-12T08:31:10Z)
• Out-of-Town Recommendation with Travel Intention Modeling [45.61038400572223]
  Out-of-town recommendation is designed for those users who leave their home-town areas and visit the areas they have never been to before. It is challenging to recommend Point-of-Interests (POIs) for out-of-town users since the out-of-town check-in behavior is determined by not only the user's home-town preference but also the user's travel intention. We propose a TRAvel-INtention-aware Out-of-town Recommendation framework, named TRAINOR.
  arXiv  Detail & Related papers  (2021-01-29T13:14:29Z)
• A Serious Game Approach for the Electro-Mobility Sector [56.31696262234312]
  This paper aims to present a SG approach for the electro-mobility context, in order to encourage the use of electric light vehicles. The design of the SG is based on the typical elements of the classic "game" with a real gameplay with different purposes.
  arXiv  Detail & Related papers  (2020-12-01T13:41:35Z)

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.