Relevant Words Extraction Method in Text Mining

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction. Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based Technique based on Compiling Technique. Context- free grammar is very suitable for relevant words extraction. The extracted keys will be used in recommendation system. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. In recommendation, this system proposed Content-based Filtering approach with Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Relevant Words Extraction Method in Text Mining

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction.  Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based Technique based on Compiling Technique. Context- free grammar is very suitable for relevant words extraction. The extracted keys will be used in recommendation system. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. In recommendation, this system proposed Content-based Filtering approach with Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Relevant Words Extraction Method for Recommendation System

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction. Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based approach in Compiling Technique. Context- free grammar is the most suitable for relevant words extraction. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. This system implements efficient recommendation system by using proposed key extraction algorithm, Content-based Filtering (CBF) method and Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Relevant Words Extraction Method for Recommendation System

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction.  Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based approach in Compiling Technique. Context- free grammar is the most suitable for relevant words extraction. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. This system implements efficient recommendation system by using proposed key extraction algorithm, Content-based Filtering (CBF) method and Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation

We proposes a novel deep neural network based recommendation model named Convolutional and Dense-layer Matrix Factorization (CDMF) for Context-aware recommendation, which is to combine multi-source information from item description and tag information. CDMF adopts a convolution neural network to extract hidden feature from item description as document and then fuses it with tag information via a full connection layer, thus generates a comprehensive feature vector. Based on the matrix factorization method, CDMF makes rating prediction based on the fused information of both users and items. Experiments on a real dataset show that the proposed deep learning model obviously outperforms the state-of-art recommendation methods

Tensor Factorization with Label Information for Fake News Detection

The buzz over the so-called "fake news" has created concerns about a degenerated media environment and led to the need for technological solutions. As the detection of fake news is increasingly considered a technological problem, it has attracted considerable research. Most of these studies primarily focus on utilizing information extracted from textual news content. In contrast, we focus on detecting fake news solely based on structural information of social networks. We suggest that the underlying network connections of users that share fake news are discriminative enough to support the detection of fake news. Thereupon, we model each post as a network of friendship interactions and represent a collection of posts as a multidimensional tensor. Taking into account the available labeled data, we propose a tensor factorization method which associates the class labels of data samples with their latent representations. Specifically, we combine a classification error term with the standard factorization in a unified optimization process. Results on real-world datasets demonstrate that our proposed method is competitive against state-of-the-art methods by implementing an arguably simpler approach.Comment: Presented at the Workshop on Reducing Online Misinformation Exposure ROME 201

Combining privileged information to improve context-aware recommender systems

A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems. The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II). We evaluated the contextual information in four context-aware recommender systems. Different weights were assigned to each type of information. The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.FAPESP (grant #2010/20564-8, #2012/13830-9, and #2013/16039-3, São Paulo Research Foundation (FAPESP))CAPE