Recommendation system can greatly alleviate the "information overload" in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-it...Recommendation system can greatly alleviate the "information overload" in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation methods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED(PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis(PLSA) to extract users' interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean distance is adopted to compute the users' similarity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors' ratings. We evaluate PBUED on two datasets and experimental results show PBUED can lead to better predicting performance and ranking performance than other approaches.展开更多
基金supported in part by the National High‐tech R&D Program of China (863 Program) under Grant No. 2013AA102301technological project of Henan province (162102210214)
文摘Recommendation system can greatly alleviate the "information overload" in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation methods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED(PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis(PLSA) to extract users' interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean distance is adopted to compute the users' similarity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors' ratings. We evaluate PBUED on two datasets and experimental results show PBUED can lead to better predicting performance and ranking performance than other approaches.
