Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user’s availability. While there are some prior studies which
included the temporal effect into POI recommendations, they overlooked the compatibility between time-varying popularity of POIs and regular availability of users, which we believe has a non-negligible impact on user decision-making. To this end, in this paper, we present a novel method which incorporates the degree of temporal matching between users and POIs into personalized POI recommendations. Specifically, we first profile the temporal popularity of POIs to show when a POI is popular for visit by mining the spatio-temporal human mobility and POI category data. Secondly, we propose latent user regularities to characterize when a user is regularly available for exploring POIs, which is learned with a user-POI temporal matching function. Finally, results of extensive experiments with real-world POI check-in and human mobility data demonstrate that our proposed user-POI temporal matching method delivers substantial advantages over baseline models for POI recommendation tasks.
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This content will become publicly available on December 1, 2024
IKGN: Intention-aware Knowledge Graph Network for POI Recommendation
Point-of-Interest (POI) recommendation, pivotal for
guiding users to their next interested locale, grapples with the
persistent challenge of data sparsity. Whereas knowledge graphs
(KGs) have emerged as a favored tool to mitigate the issue, existing
KG-based methods tend to overlook two crucial elements:
the intention steering users’ location choices and the high-order
topological structure within the KG. In this paper, we craft an
Intention-aware Knowledge Graph (IKG) that harmonizes users’
visit histories, movement trajectories, and location categories to
model user intentions. Building upon IKG, our novel Intentionaware
Knowledge Graph Network (IKGN) delves deeper into
the POI recommendation by weighing and propagating node
embeddings through an attention mechanism, capturing the
unique locational intent of each user. A sequential model like
GRU is then employed to ensure a comprehensive representation
of users’ short- and long-term location preferences. An empirical
study on two real-world datasets validates the effectiveness of our
proposed IKGN, with it markedly outshining seven benchmark
rival models in both Recall and NDCG metrics.
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- NSF-PAR ID:
- 10512867
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-0788-7
- Page Range / eLocation ID:
- 908 to 917
- Format(s):
- Medium: X
- Location:
- Shanghai, China
- Sponsoring Org:
- National Science Foundation
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