711 research outputs found
Decentralized Collaborative Learning Framework for Next POI Recommendation
Next Point-of-Interest (POI) recommendation has become an indispensable
functionality in Location-based Social Networks (LBSNs) due to its
effectiveness in helping people decide the next POI to visit. However, 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. Although there have been several on-device frameworks for
privacy-preserving POI recommendations, they are still resource-intensive when
it comes to storage and computation, and show limited robustness to the high
sparsity of user-POI interactions. On this basis, 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. DCLR significantly reduces the local models' dependence
on the cloud for training, and can be used to expand arbitrary centralized
recommendation models. To counteract the sparsity of on-device user data when
learning each local model, we design two self-supervision signals to pretrain
the POI representations on the server with geographical and categorical
correlations of POIs. To facilitate collaborative learning, we innovatively
propose to incorporate knowledge from either geographically or semantically
similar users into each local model with attentive aggregation and mutual
information maximization. The collaborative learning process makes use of
communications between devices while requiring only minor engagement from the
central server for identifying user groups, and is compatible with common
privacy preservation mechanisms like differential privacy. We evaluate DCLR
with two real-world datasets, where the results show that DCLR outperforms
state-of-the-art on-device frameworks and yields competitive results compared
with centralized counterparts.Comment: 21 Pages, 3 figures, 4 table
BERT4Loc: BERT for Location -- POI Recommender System
Recommending points of interest is a difficult problem that requires precise
location information to be extracted from a location-based social media
platform. Another challenging and critical problem for such a location-aware
recommendation system is modelling users' preferences based on their historical
behaviors. We propose a location-aware recommender system based on
Bidirectional Encoder Representations from Transformers for the purpose of
providing users with location-based recommendations. The proposed model
incorporates location data and user preferences. When compared to predicting
the next item of interest (location) at each position in a sequence, our model
can provide the user with more relevant results. Extensive experiments on a
benchmark dataset demonstrate that our model consistently outperforms a variety
of state-of-the-art sequential models
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