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Deep association neural network (DAN) 1

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional 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 algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s 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 algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . 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 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 s can provide better performance.

Keywords： Neural network Deep learning Deep association neural network (DAN) Recommendation

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Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An Industrial

Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu

Engineering 2023, Volume 22, Issue 3, Pages 14-19 doi: 10.1016/j.eng.2021.08.018

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A knowledge push technology based on applicable probability matching and multidimensional context driving None

Shu-you ZHANG, Ye GU, Xiao-jian LIU, Jian-rong TAN

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 2, Pages 235-245 doi: 10.1631/FITEE.1700763

Abstract: Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push’, can help im-prove the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intel-ligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

Keywords： Product design Knowledge push Applicable probability matching Multidimensional context Personalization