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Study on Tag, Trust and Probability Matrix Factorization Based Social Network Recommendation
( Zhigang Liu ),( Haidong Zhong ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.5
In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users’ preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available Last.fm dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.
A prediction model of the sum of container based on combined BP neural network and SVM
Min-jie Ding,Shao-zhong Zhang,Haidong Zhong,Yao-hui Wu,Liang-bin Zhang 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.2
The prediction of the sum of container is very important in the field of container transport. Many influencingfactors can affect the prediction results. These factors are usually composed of many variables, whosecomposition is often very complex. In this paper, we use gray relational analysis to set up a proper forecastindex system for the prediction of the sum of containers in foreign trade. To address the issue of the lowaccuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factorsand other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP)neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalizedby the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residualcorrection calculation for the results based on the preliminary data. The results of practical examples show thatthe overall relative error of the combined prediction model is no more than 1.5%, which is less than the relativeerror of the single prediction models. It is hoped that the research can provide a useful reference for theprediction of the sum of container and related studies.