A new document representation based on global policy for supervised term weighting schemes in text categorization.

There are two main factors involved in documents classification, document representation method and classification algorithm. In this study, we focus on document representation method and demonstrate that the choice of representation methods has impacts on quality of classification results. We propose a document representation strategy for supervised text classification named document representation based on global policy (DRGP), which can obtain an appropriate document representation according to the distribution of terms. The main idea of DRGP is to construct the optimization function through the importance of terms to different categories. In the experiments, we investigate the effects of DRGP on the 20 Newsgroups, Reuters21578 datasets, and using the SVM as classifier. The results show that the DRGP outperforms other text representation strategy schemes, such as Document Max, Document Two Max and global policy.

Adaptive Deep Modeling of Users and Items Using Side Information for Recommendation.

In the existing recommender systems, matrix factorization (MF) is widely applied to model user preferences and item features by mapping the user-item ratings into a low-dimension latent vector space. However, MF has ignored the individual diversity where the user's preference for different unrated items is usually different. A fixed representation of user preference factor extracted by MF cannot model the individual diversity well, which leads to a repeated and inaccurate recommendation. To this end, we propose a novel latent factor model called adaptive deep latent factor model (ADLFM), which learns the preference factor of users adaptively in accordance with the specific items under consideration. We propose a novel user representation method that is derived from their rated item descriptions instead of original user-item ratings. Based on this, we further propose a deep neural networks framework with an attention factor to learn the adaptive representations of users. Extensive experiments on Amazon data sets demonstrate that ADLFM outperforms the state-of-the-art baselines greatly. Also, further experiments show that the attention factor indeed makes a great contribution to our method.