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Collaborative filtering algorithms make use of interactions rates between users and items for generating recommendations. Similarity among users or items is calculated based on rating mostly, without considering explicit properties of users or items involved. In this paper, we proposed collaborative filtering algorithm using topic model. We describe user-item matrix as document-word matrix and user...
Sentence ordering is different but important for multi-document summarization. In this paper, we propose a topic approach to sentence ordering for generic multi-document summarization. We use LDA model to calculate sentence weight and similarity between sentence and topic. We put these two criteria into two-dimensional coordinate system in order to integrate them. We illustrate the problem of sentence...
Latent Dirichlet Allocation (LDA) has been used to generate text corpora topics recently. The basic idea of most LDA is that documents are represented as random mixtures over latent topics, each topic is characterized by a distribution over words. However, the main task of multi-document summarization is sentences selection. For generic multi-document summarization, we propose a multi-document summarization...
Based on LDA(Latent Dirichlet Allocation) topic model, a generative model for multi-document summarization, namely Titled-LDA that simultaneously models the content of documents and the titles of document is proposed. This generative model represents each document with a mixture of topics, and extends these approaches to title modeling by allowing the mixture weights for topics to be determined by...
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