Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： Collaborative filtering plays an important role in recommendation system and is the most successful and widely used technology in information filtering and information system. However, collaborative filtering has a sparse problem in data processing, which affects the accuracy of the proposed algorithm. This paper proposed a recommendation algorithm combining collaborative filtering and XGBoost to explore the potential relationship between the project and the user based on the user's evaluation of the project and its own characteristics. It improved the recommendation accuracy of the algorithm. The results of experiments on the book-crossings data set using the baidu deep learning framework paddlepaddlepaddles show that, Compared with the two algorithms in the literature, the accuracy of the proposed algorithm is significantly improved.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-24 Cooperative journals: 《计算机应用研究》
Abstract： The selection of feature items and the calculation of feature weights are two crucial links in the process of text classification and play a key role in the results of text classification. In order to overcome the traditional CHI statistical method, there is a negative correlation between the frequency of feature items and the category, and a probability problem that a feature item exists in a text, The traditional CHI statistical method is improved by introducing some important factors such as negative correlation judgment and frequency, and the TF-IDF algorithm is optimized by combining the calculation method of semantic similarity. The K-nearest neighbor (KNN) classifier and support vector machine (SVM) classifier are respectively used in WEKA software to classify the Weibo emotional corpus The experimental results show that the new method has obvious improvement on the accuracy of text classification.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-19 Cooperative journals: 《计算机应用研究》
Abstract： This paper proposed a linear regression algorithm to integrate collaborative filtering based on the data sparse influence of the traditional collaborative filtering algorithm. Firstly, it built a similarity matrix between the user and the project based on the user's rating of the project, as well as the user and the project's own characteristics. Secondly, based on the similarity matrix, it selected the user and project nearest neighbor set. It predicted the score that the users had graded respectively by the way of collaborative filtering algorithms based on the user and the project. And it would take the difference between predicted scores and the real scores as features to generate new training data, and regard the new training data as the input of the linear regression model. Finally, according to the training model, it could predict the unknown score , and used the Top-N algorithm to generate the recommended list. It conducted the experiment on the MovieLens data set. The experimental result shows that the proposed accuracy of the new algorithm improves compared with the traditional collaborative filtering algorithm.
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