首页|Spatio-temporal intention learning for recommendation of next point-of-interest
Spatio-temporal intention learning for recommendation of next point-of-interest
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NETL
NSTL
万方数据
Next point-of-interest(POI)recommendation has been applied by many internet companies to enhance the user travel experience.Recent research advocates deep-learning methods to model long-term check-in sequences and mine mobility patterns of people to improve recom-mendation performance.Existing approaches model general user preferences based on histor-ical check-ins and can be termed as preference pattern models.The preference pattern is different from the intention pattern,in that it does not emphasize the user mobility pattern of revisiting POIs,which is a common behavior and kind of intention for users.An effective module is needed to predict when and where users will repeat visits.In this paper,we propose a Spatio-Temporal Intention Learning Self-Attention Network(STILSAN)for next POI recommendation.STILSAN employs a preference-intention module to capture the user's long-term preference and recognizes the user's intention to revisit some specific POIs at a specific time.Meanwhile,we design a spatial encoder module as a pretrained model for learning POI spatial feature by simulating the spatial clustering phenomenon and the spatial proximity of the POIs.Experiments are conducted on two real-world check-in datasets.The experimental results demonstrate that all the proposed modules can effectively improve recommendation accuracy and STILSAN yields outstanding improvements over the state-of-the-art models.
NETL
NSTL
万方数据
