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To address the problem of poor recommendation accuracy in the session recommendation model, this paper proposed a capsule-based graph convolutional disentanglement for session-aware recommendation (CGCD) method.Specifically, it used disentangled learning technique to transition item embedding into factor embedding based on multiple sub-channels. Then, it used a capsule dynamic fusion strategy to aggregate different factors to obtain a new item embedding.In addition, it used a multi-head attention mechanism to assign weights to each item in the session. Finally, it aggregated the item embedding with other items in the current session according to the assigned weights, and then generated an accurate session representation to achieve item recommendation. Experiments on two publicly available real datasets show that the proposed model can improve the Pre@10, Pre@20, MRR@10 and MRR@20 by 5.17%, 2.99%, 6.56% and 2.94% on average of the recommended performance, which verifies the effectiveness and efficiency of the method in this paper. [ABSTRACT FROM AUTHOR]

With the advances in artificial intelligence, machines can now integrate information and extract its features to build a predictive model of likely outcomes, providing more insights to make better decisions. An artificial neural network, which is based on the structure and functions of the human brain, is a series of algorithms that can filter information into layers of recognizable characteristics. The computational power of neural networks is increasing in parallel with improved development of hardware devices. Artificial intelligence has become inseparable from our daily lives because it assists us in many areas. In recent years, researchers in sports have begun to incorporate the tools of artificial intelligence to complete many tasks that used to rely on substantial labor inputs. For instance, player and object trajectory tracking makes it easier to understand team play tactics and to analyze the strategies used in games. Motion recognition provides broadcasting and live-streaming programs with quick information on what skills are deployed. Data analysis can prevent athletes from sustaining injuries. Thanks to the continual improvements in artificial intelligence, more and more studies on its application in sports are expected. Language-teaching and referee robots are likely to be introduced in the near future. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the existing modeling methods of combining metadata are based on the same user I item attribute weights, so that the key relationships between users and items are not significant, and it is difficult to obtain better recommendation performance. To solve the above problems, this paper proposed a method of deep joint learning recommendation based on metadata and attention mechanism. It used double deep network joint learning, one of the networks implemented matrix nonlinear decomposition based on implicit feedback data to learn user/ project personalization relationship, and another network automatically captured influence the user/ item key attributes to recommend by using attention mechanism, through the user preference relation with weighted of different attributes modeling highlighted the extended model. Experimental results show that the proposed recommendation algorithm has better recommendation performance on two public datasets of MovieLens 100K and MovieLens lM. [ABSTRACT FROM AUTHOR]