Purpose:The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems.However,the existing algorithms rely mostly on common ratings of items and do not consider tempor...Purpose:The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems.However,the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests.To solve this problem,this study proposes a new user-item composite filtering(UICF)recommendation framework by leveraging temporal semantics.Design/methodology/approach:The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’latest interest degrees.For an item to be probably recommended to a user,the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.Findings:Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework.Experimental results show that the UICF outperformed three well-known recommendation algorithms ItemBased Collaborative Filtering(IBCF),User-Based Collaborative Filtering(UBCF)and User-Popularity Composite Filtering(UPCF)in the root mean square error(RMSE),mean absolute error(MAE)and F1 metrics,especially yielding an average decrease of 11.9%in MAE.Originality/value:A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model.It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively,resulting in more accurate and personalized recommendations.展开更多
The research on Temporal Databases (TDB) has been a hot topic for quite along time, but few implementations have been reported. The authors have developeda prototroe of temporal DBMS in DOS/Windows envirorunent, calle...The research on Temporal Databases (TDB) has been a hot topic for quite along time, but few implementations have been reported. The authors have developeda prototroe of temporal DBMS in DOS/Windows envirorunent, calledHBase. This paper discusses its temporal structure, temporal syntax and semanics,as well as the special techniques used in the Anplementation of HBase.展开更多
基金supported by the National Natural Science Foundations of China(Grant No.62362050,Grant No.62266032)Jiangxi Training Program for Academic and Technical Leaders in Major Disciplines-Leading Talents Project(Grant No.20225BCI22016).
文摘Purpose:The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems.However,the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests.To solve this problem,this study proposes a new user-item composite filtering(UICF)recommendation framework by leveraging temporal semantics.Design/methodology/approach:The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’latest interest degrees.For an item to be probably recommended to a user,the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.Findings:Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework.Experimental results show that the UICF outperformed three well-known recommendation algorithms ItemBased Collaborative Filtering(IBCF),User-Based Collaborative Filtering(UBCF)and User-Popularity Composite Filtering(UPCF)in the root mean square error(RMSE),mean absolute error(MAE)and F1 metrics,especially yielding an average decrease of 11.9%in MAE.Originality/value:A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model.It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively,resulting in more accurate and personalized recommendations.
文摘The research on Temporal Databases (TDB) has been a hot topic for quite along time, but few implementations have been reported. The authors have developeda prototroe of temporal DBMS in DOS/Windows envirorunent, calledHBase. This paper discusses its temporal structure, temporal syntax and semanics,as well as the special techniques used in the Anplementation of HBase.
