Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming ...Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming to predict a user's rating for those items which were not rated yet by the user. However, with the increasing number of items and users, thedata is sparse.It is difficult to detectlatent closely relation among the items or users for predicting the user behaviors. In this paper,we enhance the rating prediction approach leading to substantial improvement of prediction accuracy by categorizing according to the genres of movies. Then the probabilities that users are interested in the genres are computed to integrate the prediction of each genre cluster. A novel probabilistic approach based on the sentiment analysis of the user reviews is also proposed to give intuitional explanations of why an item is recommended.To test the novel recommendation approach, a new corpus of user reviews on movies obtained from the Internet Movies Database(IMDB) has been generated. Experimental results show that the proposed framework is effective and achieves a better prediction performance.展开更多
This paper focuses on the problem of seeking complete axiomatization for finite processes in the process calculus called symbolic probabilistic π-calculus introduced by Wu,Palamidessi and Lin.We provide inference sys...This paper focuses on the problem of seeking complete axiomatization for finite processes in the process calculus called symbolic probabilistic π-calculus introduced by Wu,Palamidessi and Lin.We provide inference systems for both strong and weak symbolic probabilistic bisimulations and also prove their soundness and completeness.This is the first work,to our knowledge,that provides complete axiomatization for symbolic probabilistic bisimulations in the presence of both nondeterministic and probabilistic choice.展开更多
基金supported in part by National Science Foundation of China under Grants No.61303105 and 61402304the Humanity&Social Science general project of Ministry of Education under Grants No.14YJAZH046+2 种基金the Beijing Natural Science Foundation under Grants No.4154065the Beijing Educational Committee Science and Technology Development Planned under Grants No.KM201410028017Academic Degree Graduate Courses group projects
文摘Collaborative Filtering(CF) is a leading approach to build recommender systems which has gained considerable development and popularity. A predominant approach to CF is rating prediction recommender algorithm, aiming to predict a user's rating for those items which were not rated yet by the user. However, with the increasing number of items and users, thedata is sparse.It is difficult to detectlatent closely relation among the items or users for predicting the user behaviors. In this paper,we enhance the rating prediction approach leading to substantial improvement of prediction accuracy by categorizing according to the genres of movies. Then the probabilities that users are interested in the genres are computed to integrate the prediction of each genre cluster. A novel probabilistic approach based on the sentiment analysis of the user reviews is also proposed to give intuitional explanations of why an item is recommended.To test the novel recommendation approach, a new corpus of user reviews on movies obtained from the Internet Movies Database(IMDB) has been generated. Experimental results show that the proposed framework is effective and achieves a better prediction performance.
基金the National Basic Research Program (973) of China (No.2003CB317005)the National Nature Science Foundation of China (Nos.60573002 and 60703033)
文摘This paper focuses on the problem of seeking complete axiomatization for finite processes in the process calculus called symbolic probabilistic π-calculus introduced by Wu,Palamidessi and Lin.We provide inference systems for both strong and weak symbolic probabilistic bisimulations and also prove their soundness and completeness.This is the first work,to our knowledge,that provides complete axiomatization for symbolic probabilistic bisimulations in the presence of both nondeterministic and probabilistic choice.
