Online lodging platforms have become more and more popular around the world.To make a booking in these platforms,a user usually needs to select a city first,then browses among all the prospective options.To improve th...Online lodging platforms have become more and more popular around the world.To make a booking in these platforms,a user usually needs to select a city first,then browses among all the prospective options.To improve the user experience,understanding the zone preferences of a user's booking behavior will be helpful.In this work,we aim to predict the zone preferences of users when booking accommodations for the next travel.We have two main challenges:(1)The previous works about next information of Points Of Interest(Pals)recommendation are mainly focused on users'historical records in the same city,while in practice,the historical records of a user in the same city would be very sparse.(2)Since each city has its own specific geographical entities,it is hard to extract the structured geographical features of accommodation in different cities.Towards the difficulties,we propose DeepPredict,a zone preference prediction system.To tackle the first challenge,DeepPredict involves users'historical records in all the cities and uses a deep learning based method to process them.For the second challenge,DeepPredict uses HERE places API to get the information of pals nearby,and processes the information with a unified way to get it.Also,the description of each accommodation might include some useful information,thus we use Sent2Vec,a sentence embedding algorithm,to get the embedding of accommodation description.Using a real-world dataset collected from Airbnb,DeepPredict can predict the zone preferences of users'next bookings with a remarkable performance.DeepPredict outperforms the state-of-the-art algorithms by 60%in macro Fl-score.展开更多
In this paper,we present a probabilistic numerical method for a class of forward utilities in a stochastic factor model.For this purpose,we use the representation of forward utilities using the ergodic Backward Stocha...In this paper,we present a probabilistic numerical method for a class of forward utilities in a stochastic factor model.For this purpose,we use the representation of forward utilities using the ergodic Backward Stochastic Differential Equations(eBSDEs)introduced by Liang and Zariphopoulou in[27].We establish a connection between the solution of the ergodic BSDE and the solution of an associated BSDE with random terminal time T,defined as the hitting time of the positive recurrent stochastic factor.The viewpoint based on BSDEs with random horizon yields a new characterization of the ergodic cost^which is a part of the solution of the eBSDEs.In particular,for a certain class of eBSDEs with quadratic generator,the Cole-Hopf transformation leads to a semi-explicit representation of the solution as well as a new expression of the ergodic cost>.The latter can be estimated with Monte Carlo methods.We also propose two new deep learning numerical schemes for eBSDEs.Finally,we present numerical results for different examples of eBSDEs and forward utilities together with the associated investment strategies.展开更多
The advent of digital technology has spawned a revolution in software engineering,with artificial intelligence(AD)emerging as a key technology.The integration of advanced techniques,such as natural language processing...The advent of digital technology has spawned a revolution in software engineering,with artificial intelligence(AD)emerging as a key technology.The integration of advanced techniques,such as natural language processing(NLP)and deep learning has demonstrated AI's remarkable capabilities throughout the software development lifecycle.Enhancements in areas such as code generation,code inspection,and software testing have significantly elevated both efficiency and quality.In addition,the potential of AI also provides new possibilities for automated software updates and maintenance in the future.However,despite the broad application prospects of AI in software engineering,it still faces some problems to be solved urgently.For example,inadequate adaptability and the challenges of personal data privacy protection limit its wider application.At the same time,the high research cost and immature model technology also bring obstacles to further development.By comprehensively analyzing the existing literature and related cases,this study deeply discusses the application status and limitations of AI in software engineering.The research results show that although AI can greatly improve the efficiency of software development,its shortcomings in data security and adaptability need attention.Futuure research should address these problems and seek more effective technical solutions to promote the sustainable development of AI in software engineering.展开更多
基金This work was sponsored by the National Natural Science Foundation of China(Nos.71731004,62072115,61602122,and 61971145)Shanghai Pujiang Program(No.2020PJD005)+1 种基金the Research Grants Council of Hong Kong(No.16214817)the 5GEAR Project and FIT Project from the Academy of Finland.
文摘Online lodging platforms have become more and more popular around the world.To make a booking in these platforms,a user usually needs to select a city first,then browses among all the prospective options.To improve the user experience,understanding the zone preferences of a user's booking behavior will be helpful.In this work,we aim to predict the zone preferences of users when booking accommodations for the next travel.We have two main challenges:(1)The previous works about next information of Points Of Interest(Pals)recommendation are mainly focused on users'historical records in the same city,while in practice,the historical records of a user in the same city would be very sparse.(2)Since each city has its own specific geographical entities,it is hard to extract the structured geographical features of accommodation in different cities.Towards the difficulties,we propose DeepPredict,a zone preference prediction system.To tackle the first challenge,DeepPredict involves users'historical records in all the cities and uses a deep learning based method to process them.For the second challenge,DeepPredict uses HERE places API to get the information of pals nearby,and processes the information with a unified way to get it.Also,the description of each accommodation might include some useful information,thus we use Sent2Vec,a sentence embedding algorithm,to get the embedding of accommodation description.Using a real-world dataset collected from Airbnb,DeepPredict can predict the zone preferences of users'next bookings with a remarkable performance.DeepPredict outperforms the state-of-the-art algorithms by 60%in macro Fl-score.
基金The authors research is part of the ANR project DREAMeS(ANR-21-CE46-0002)and benefited from the support of respectively the "Chair Risques Emergents en Assurance"and"Chair Impact de la Transition Climatique en Assurance"under the aegis of Fondation du Risque,a joint initiative by Risk and Insurance Institute of Le Mans,and MMA-Covea and Groupama respectively.
文摘In this paper,we present a probabilistic numerical method for a class of forward utilities in a stochastic factor model.For this purpose,we use the representation of forward utilities using the ergodic Backward Stochastic Differential Equations(eBSDEs)introduced by Liang and Zariphopoulou in[27].We establish a connection between the solution of the ergodic BSDE and the solution of an associated BSDE with random terminal time T,defined as the hitting time of the positive recurrent stochastic factor.The viewpoint based on BSDEs with random horizon yields a new characterization of the ergodic cost^which is a part of the solution of the eBSDEs.In particular,for a certain class of eBSDEs with quadratic generator,the Cole-Hopf transformation leads to a semi-explicit representation of the solution as well as a new expression of the ergodic cost>.The latter can be estimated with Monte Carlo methods.We also propose two new deep learning numerical schemes for eBSDEs.Finally,we present numerical results for different examples of eBSDEs and forward utilities together with the associated investment strategies.
文摘The advent of digital technology has spawned a revolution in software engineering,with artificial intelligence(AD)emerging as a key technology.The integration of advanced techniques,such as natural language processing(NLP)and deep learning has demonstrated AI's remarkable capabilities throughout the software development lifecycle.Enhancements in areas such as code generation,code inspection,and software testing have significantly elevated both efficiency and quality.In addition,the potential of AI also provides new possibilities for automated software updates and maintenance in the future.However,despite the broad application prospects of AI in software engineering,it still faces some problems to be solved urgently.For example,inadequate adaptability and the challenges of personal data privacy protection limit its wider application.At the same time,the high research cost and immature model technology also bring obstacles to further development.By comprehensively analyzing the existing literature and related cases,this study deeply discusses the application status and limitations of AI in software engineering.The research results show that although AI can greatly improve the efficiency of software development,its shortcomings in data security and adaptability need attention.Futuure research should address these problems and seek more effective technical solutions to promote the sustainable development of AI in software engineering.
