THESIS
2022
1 online resource (xiii, 71 pages) : illustrations (some color)
Abstract
In this thesis, We study the application of two types of recommender systems.
In the rst part, we propose a novel tensor-based method to provide POI recommendations
to users. Location-based social networks(LBSNs) have attracted
millions of users to share their social friendship and daily activity locations.
Plenty of the available check-in records make it possible to mine users preferences
on locations and provided POI recommendations to users. However, It is a
challenge to learn the preference of users since check-in data are sparse, long-tail,
and inuenced by time and social relationships. Our proposed method can simultaneously
deal with the above issues and experiments on a real-world dataset
show that our model could provide more accurate location recommendations to
users.
We illust...[
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In this thesis, We study the application of two types of recommender systems.
In the rst part, we propose a novel tensor-based method to provide POI recommendations
to users. Location-based social networks(LBSNs) have attracted
millions of users to share their social friendship and daily activity locations.
Plenty of the available check-in records make it possible to mine users preferences
on locations and provided POI recommendations to users. However, It is a
challenge to learn the preference of users since check-in data are sparse, long-tail,
and inuenced by time and social relationships. Our proposed method can simultaneously
deal with the above issues and experiments on a real-world dataset
show that our model could provide more accurate location recommendations to
users.
We illustrate a novel SAVER model for handling a dynamic treatment recommendation
setting involving multiple diseases and medications in the second part.
Most of the existing works based on reinforcement learning just construct the
reward function and state simply since it is a hard task to nd a proper metric
to evaluate the condition of a patient with multi-morbidity and to model the
complicated daily condition of patients. In this thesis, a novel reward function is dened to provide a preliminary signal for which action is responsible for a good
or bad outcome. A state with a more complicated structure is used to capture
more features of a patients condition. Besides, a supervised constraint is fused
into a reinforcement learning framework to avoid unacceptable risks.
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