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  • Open Access


    Popularity Prediction of Social Media Post Using Tensor Factorization

    Navdeep Bohra1,2, Vishal Bhatnagar3, Amit Choudhary4, Savita Ahlawat2, Dinesh Sheoran2, Ashish Kumari2,*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 205-221, 2023, DOI:10.32604/iasc.2023.030708

    Abstract The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about… More >

  • Open Access


    Recommender Systems Based on Tensor Decomposition

    Zhoubao Sun1,*, Xiaodong Zhang1, Haoyuan Li1, Yan Xiao2, Haifeng Guo3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 621-630, 2021, DOI:10.32604/cmc.2020.012593

    Abstract Recommender system is an effective tool to solve the problems of information overload. The traditional recommender systems, especially the collaborative filtering ones, only consider the two factors of users and items. While social networks contain abundant social information, such as tags, places and times. Researches show that the social information has a great impact on recommendation results. Tags not only describe the characteristics of items, but also reflect the interests and characteristics of users. Since the traditional recommender systems cannot parse multi-dimensional information, in this paper, a tensor decomposition model based on tag regularization is More >

  • Open Access


    TdBrnn: An Approach to Learning Users’ Intention to Legal Consultation with Normalized Tensor Decomposition and Bi-LSTM

    Xiaoding Guo1, Hongli Zhang1, *, Lin Ye1, Shang Li1

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 315-336, 2020, DOI:10.32604/cmc.2020.07506

    Abstract With the development of Internet technology and the enhancement of people’s concept of the rule of law, online legal consultation has become an important means for the general public to conduct legal consultation. However, different people have different language expressions and legal professional backgrounds. This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation. How to accurately understand the true intentions behind different users’ legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services. Traditional intent understanding algorithms rely heavily… More >

  • Open Access


    Parameters Compressing in Deep Learning

    Shiming He1, Zhuozhou Li1, Yangning Tang1, Zhuofan Liao1, Feng Li1, *, Se-Jung Lim2

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.06130

    Abstract With the popularity of deep learning tools in image decomposition and natural language processing, how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved. These parameters are huge and can be as many as millions. At present, a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure. These methods include matrix decomposition and tensor decomposition. To let vector take advance of the compressing performance of More >

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