Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflec...Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive users.To better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal context.First,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window.Subsequently,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users.We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,respectively.Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics.More specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.展开更多
Obesity is a problem with global health and economic consequences. This study assesses the impact of social origins and socio-economic disadvantages on mid-life body weight. Hypotheses of both critical early life peri...Obesity is a problem with global health and economic consequences. This study assesses the impact of social origins and socio-economic disadvantages on mid-life body weight. Hypotheses of both critical early life period and social trajectory were tested using a sample of 845 adults, aged 45 to 69 years, from a nationally representative sample in 2009 Taiwan Social Change Survey. The study found evidence of a significant gender-specific heterogeneity of social origins on obesity risk. Men with accumulated disadvantage had increased BMI, but no significant accumulative trajectories were found among women. Obesity prevention must consider factors beyond behavioral change, and include a focus on social origins and gender identity.展开更多
基金the National Natural Science Foundation of China(Grant Nos.62102347,62376041,62172352)Guangdong Ocean University Research Fund Project(Grant No.060302102304).
文摘Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive users.To better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal context.First,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window.Subsequently,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users.We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,respectively.Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics.More specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.
文摘Obesity is a problem with global health and economic consequences. This study assesses the impact of social origins and socio-economic disadvantages on mid-life body weight. Hypotheses of both critical early life period and social trajectory were tested using a sample of 845 adults, aged 45 to 69 years, from a nationally representative sample in 2009 Taiwan Social Change Survey. The study found evidence of a significant gender-specific heterogeneity of social origins on obesity risk. Men with accumulated disadvantage had increased BMI, but no significant accumulative trajectories were found among women. Obesity prevention must consider factors beyond behavioral change, and include a focus on social origins and gender identity.
