DAN:a deep association neural network approach for personalization

The collaborative filtering technology used in traditional recommendation systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional recommendation algorithms, thus leading to the emergence of recommendation systems based on deep learning. At present, deep learning recommendations mostly use deep neural networks to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep neural network recommendation algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the recommendation. Aimed at this problem, in this paper we propose a feedforward deep neural network recommendation method, called the deep association neural network(DAN), which is based on the joint action of multiple categories of information, for implicit feedback recommendation. Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the recommendation is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint recommendations can provide better recommendation performance.

Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction

Traditional researches on user preferences mining mainly explore the user’s overall preferences on the project,but ignore that the fundamental motivation of user preferences comes from their attitudes on some attributes of the project.In addition,traditional researches seldom consider the typical preferences combination of group users,which may have influence on the personalized service for group users.To solve this problem,a method with noise reduction for group user preferences mining is proposed,which focuses on mining the multi-attribute preference tendency of group users.Firstly,both the availability of data and the noise interference on preferences mining are considered in the algorithm design.In the process of generating group user preferences,a new path is used to generate preference keywords so as to reduce the noise interference.Secondly,the Gibbs sampling algorithm is used to estimate the parameters of the model.Finally,using the user comment data of several online shopping websites as experimental objects,the method is used to mine the multi-attribute preferences of different groups.The proposed method is compared with other methods from three aspects of predictive ability,preference mining ability and preference topic similarity.Experimental results show that the method is significantly better thap other existing methods.