A hybrid collaborative filtering algorithm based on the user preferences and item features is proposed.A thorough investigation of Collaborative Filtering （CF） techniques preceded the development of this algorithm.T...A hybrid collaborative filtering algorithm based on the user preferences and item features is proposed.A thorough investigation of Collaborative Filtering （CF） techniques preceded the development of this algorithm.The proposed algorithm improved the user-item similarity approach by extracting the item feature and applying various item features＇ weight to the item to confirm different item features.User preferences for different item features were obtained by employing user evaluations of the items.It is expected that providing better recommendations according to preferences and features would improve the accuracy and efficiency of recommendations and also make it easier to deal with the data sparsity.In addition,it is expected that the potential semantics of the user evaluation model would be revealed.This would explain the recommendation results and increase accuracy.A portion of the MovieLens database was used to conduct a comparative experiment among the proposed algorithms,i.e.,the collaborative filtering algorithm based on the item and the collaborative filtering algorithm based on the item feature.The Mean Absolute Error （MAE） was utilized to conduct performance testing.The experimental results show that employing the proposed personalized recommendation algorithm based on the preference-feature would significantly improve the accuracy of evaluation predictions compared to two previous approaches.展开更多
The low carbon energy transition has attracted worldwide attention to mitigate climate change.Renewable energy(RE)is the key to this transition,with significant developments to date,especially in China.This study syst...The low carbon energy transition has attracted worldwide attention to mitigate climate change.Renewable energy(RE)is the key to this transition,with significant developments to date,especially in China.This study systematically reviews the literature on RE development to identify a general context from many studies.The goal is to clarify key questions related to RE development from the current academic community.We first identify the forces driving RE development.Thereafter,we analyze methods for modeling RE developments considering the systematic and multiple complexity characteristics of RE.The study concludes with insights into the target selection and RE development roadmap in China.展开更多
基金supported in part by the National HighTech Research and Development (863) Program of China (No. 2011AA010101)the National Natural Science Foundation of China (Nos. 61103197 and 61073009)+2 种基金the Science and Technology Key Project of Jilin Province (No. 2011ZDGG007)the Youth Foundation of Jilin Province of China (No. 201101035)the Fundamental Research Funds for the Central Universities of China (No. 200903179)
文摘A hybrid collaborative filtering algorithm based on the user preferences and item features is proposed.A thorough investigation of Collaborative Filtering （CF） techniques preceded the development of this algorithm.The proposed algorithm improved the user-item similarity approach by extracting the item feature and applying various item features＇ weight to the item to confirm different item features.User preferences for different item features were obtained by employing user evaluations of the items.It is expected that providing better recommendations according to preferences and features would improve the accuracy and efficiency of recommendations and also make it easier to deal with the data sparsity.In addition,it is expected that the potential semantics of the user evaluation model would be revealed.This would explain the recommendation results and increase accuracy.A portion of the MovieLens database was used to conduct a comparative experiment among the proposed algorithms,i.e.,the collaborative filtering algorithm based on the item and the collaborative filtering algorithm based on the item feature.The Mean Absolute Error （MAE） was utilized to conduct performance testing.The experimental results show that employing the proposed personalized recommendation algorithm based on the preference-feature would significantly improve the accuracy of evaluation predictions compared to two previous approaches.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.71573121 and 71834003).
文摘The low carbon energy transition has attracted worldwide attention to mitigate climate change.Renewable energy(RE)is the key to this transition,with significant developments to date,especially in China.This study systematically reviews the literature on RE development to identify a general context from many studies.The goal is to clarify key questions related to RE development from the current academic community.We first identify the forces driving RE development.Thereafter,we analyze methods for modeling RE developments considering the systematic and multiple complexity characteristics of RE.The study concludes with insights into the target selection and RE development roadmap in China.
