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In various recommender systems, ratings and reviews are the main information to show user preferences. However, recommendation models that only use ratings, such as collaborative filtering, are vulnerable to data sparsity. And models only using review information will also suffer from the sparsity of reviews. On one hand, most ratings and reviews are interrelated and complementary, reviews may explain why a user gives a high or low rating to an item. On the other hand, ratings and reviews are numerical and textual information, respectively, and they reflect the preference of the user from a coarse-grained level and a fine-grained level A user may comment positively about some aspects of an item, even he gives a very low score to this item. There are specific information among each of them because of their heterogeneity. Therefore, it is possible to learn more accurate representation of users and items by effectively integrating ratings and text reviews from different views, that is, shared-view and specific-view. In this paper, we propose a Shared-view and Specific-view Information extraction model for Recommendation (SSIR), which integrates the information from reviews and interaction matrix to predict ratings Our model has two key components, including shared-view information extraction and specific-view exploitation. From the perspective of shared-view, SSIR jointly minimizes the loss of confusion adversarial and rating prediction loss to extract the shared information from reviews and user–item interaction matrix. For the specific-view part, SSIR applies orthogonal constraints on shared-view and specific-view modules to extract the discriminative features from reviews and interaction data. We fuse the features extracted from these two views to predict the final ratings. In addition, we use auxiliary reviews to deal with the sparsity problem of reviews. Experimental results on eight datasets show the effectiveness and robustness of our method, which could adapt to the recommendation scenarios with fewer reviews and ratings. • Mining information in reviews and interaction data from shared and specific views. • Utilizing the confusion adversarial loss to extract the shared features. • Enforcing orthogonal constraints to extract the specific features. • Shared-view information and specific-view information are synergized to recommend. [ABSTRACT FROM AUTHOR]

When extracting knowledge (or patterns) from multiple databases, the data from different databases might be too large in volume to be merged into one database for centralized mining on one computer, the local information sources might be hidden from a global decision maker due to privacy concerns, and different local databases may have different contribution to the global pattern. Dealing with multiple databases is essentially different from mining from a single database. In multi-database mining, the global patterns must be obtained by carefully analyzing the local patterns from individual databases. In this paper, we propose a nonlinear method, named KEMGP (kernel estimation for mining global patterns), to tackle this problem, which adopts kernel estimation to synthesizing local patterns for global patterns. We also adopt a method to divide all the data in different databases according to attribute dimensionality, which reduces the total space complexity. We test our algorithm on a customer management system, where the application is to obtain all globally interesting patterns by analyzing the individual databases. The experimental results show that our method is efficient. [Copyright &y& Elsevier]