查看更多>>摘要：There are problems in personalised recommendation of live streaming e-commerce products, such as low accuracy in user interest mining and weak user relationship strength. Therefore, a personalised recommendation method for live streaming e-commerce products based on multimedia social networks is proposed. First, the user scoring matrix is divided into two interaction matrices by the matrix decomposition method, and the fixed parameter limit matrix dimension is set, and user interest mining is realised by using Euclidean distance calculation. Then, the variance expansion factor is introduced to test the multi-collinearity of the feature, and the contour coefficient is calculated to complete the feature extraction. Finally, user interest and feature data are introduced into multimedia social networks to obtain product feature attention, perform personalised matching, and achieve personalised recommendation. The results show that the method proposed in this paper has good user interest mining performance and strong user relationships.
查看更多>>摘要：In the process of dynamic collaborative mining of user perceived interest points on mobile e-commerce platforms, due to the lack of effective feature classification, the recall rate of interest point data in dynamic collaborative mining of interest points is low. Therefore, a dynamic collaborative mining method for user perceived interest points on mobile e-commerce platforms is proposed. Firstly, coarse grained features of user perceived interest points are initially extracted through clustering algorithms, and their feature values are further extracted using sequence feature extraction algorithms. Then, a user perceived interest prediction model is constructed, and fitting methods are used to achieve feature classification of user perceived interest points. Finally, by designing a dynamic collaborative mining model for user perceived interest points on mobile e-commerce platforms, dynamic collaborative mining is achieved. The experimental results show that the dynamic convergence change of method in this paper interest point data mining is relatively small, and the maximum recall rate is 99%, effectively improving mining performance, thereby providing more accurate and accurate personalised recommendations for mobile e-commerce platforms.
查看更多>>摘要：Due to the large scale of data, the deviation coefficient of the captured data is large and the capture efficiency is low. To this end, a large-scale Weibo location data retrieval method based on dynamic web page parsing is proposed. Firstly, based on the source of Weibo location data, artificial neural models and random functions are introduced to calculate the weights of feature data. Next, generate a feature vector table and classifier model, and filter the feature text using the established classification model. Finally, by matching the feature data of Weibo location data between dynamic script sites and web pages, a dynamic script parsing framework for Weibo location data on web pages is constructed, and dynamic web page parsing technology is used to capture Weibo location data. The experimental results show that the proposed method has only a 0.1% error in data capture bias, and the capture efficiency reaches 99%. Therefore, this method can significantly improve the crawling effect of large-scale Weibo location data and has certain feasibility.
查看更多>>摘要：To effectively reduce the dimension of cloud computing redundant data and shorten the time of dimensionality reduction, an algorithm for dimensionality reduction of cloud computing redundant data in the internet of things environment is proposed. Firstly, analyse the architecture of the internet of things environment, and cluster and collect high-dimensional redundant data of cloud computing in the internet of things environment. Secondly, the K-L transform is used to compress the redundant data of cloud computing. Finally, the supervised discriminant projection dimensionality reduction algorithm is used to construct the objective function model of redundant data dimensionality reduction to complete the dimensionality reduction of redundant data. The experimental results show that compared with traditional algorithms, the dimensionality reduction effect of our algorithm is higher, the dimension of redundant data is significantly reduced, and the dimensionality reduction time of our algorithm is significantly reduced when the data size is the same.
查看更多>>摘要：In order to improve the accuracy of cross confidence assessment and shorten the time required for confidence assessment, this article proposes a social media information confidence assessment method based on time series analysis. Firstly, determine the evaluation indicators that affect the credibility of social media information. Then, quantify the evaluation indicators for the credibility of social media information. Finally, a confidence quantitative evaluation function is constructed using time series analysis, and a user information weight allocation matrix is used to configure the weight assignment scheme for each evaluation dimension. By quantitatively calculating the relative importance between various indicators in the comparison criteria layer, the user confidence is finally obtained. The experimental results show that the method proposed in this paper can effectively improve the accuracy and recall of confidence evaluation, with a FI value of 0.9, which verifies the effectiveness of the confidence evaluation method proposed in this paper.
查看更多>>摘要：Traditional social media platforms have low accuracy in identifying false information. Therefore, a method based on multi-modal feature fusion is proposed to recognise false information within social media platforms. This method processes false information data on social media platforms by calculating noise during transmission, and utilises multi-layer management to establish correlations between multi-modal point cloud data. By designing modal grouping and calculating similarity, we integrate information from the three dimensions of time, space, and attributes to supplement the shortcomings of the data. By utilising multi-modal feature fusion algorithms, accurate recognition of false information on social media platforms can be achieved. The experimental results show that using this method can effectively improve the training accuracy of the model and have the ability to resist false data injection attacks, achieving high recognition accuracy.
查看更多>>摘要：To improve the efficiency and accuracy of supply chain risk identification, a supply chain risk identification method for foreign trade e-commerce enterprises based on social network analysis is studied. Firstly, obtain supply chain risk indicators for foreign trade e-commerce enterprises and use the LLE-PCA method to reduce the dimensionality of the indicators. Then, using social network analysis method, construct a social network model with different risk indicators interconnected. Finally, degree centrality analysis and proximity centrality analysis are used to obtain the variable values of each indicator in the model, achieving the identification of supply chain risks for foreign trade e-commerce enterprises. The experiment shows that the application of this method for risk identification takes 0.25 s, with a recognition accuracy of 82%. It has high recognition efficiency and accuracy, and the application effect is good.
查看更多>>摘要：In order to improve the accuracy and usefulness of the churn prediction model, the core elements of the research content were designed to include collecting data on customer purchase behaviour and reviews, quantifying and analysing customer value, analysing customer sentiment in reviews, and combining customer value factors and review sentiment factors in the model. The results of the study show that the model performs best on different indicators, and the area of the main characteristic curve is the largest, which is significantly higher than that of the traditional model. Its hit rate, coverage rate and improvement coefficient also perform well. At the same time, when the sample size increases, the improvement coefficient increases the most, reaching 0.41. In conclusion, the model performs well in customer churn prediction, and it can provide certain reference value for the research field of customer churn prediction.
Sina ShokoohyarVahid GhomiAmirsalar Jafari GoriziWeimin Liang...
124-154页
查看更多>>摘要：This study aims to quantify the sentiment of those discussing Airbnb on Twitter and visualise how this sentiment differed in three main periods: prior to the pandemic (pre-COVID-19), and during the pandemic before vaccines were disseminated (pre-vaccine), and during the pandemic, after vaccines were disseminated (post-vaccine). 344,705 tweets relating to Airbnb are collected. In this study, popularity, and usage analytics, sentiment analytics, voice analytics, and topic mining analytics were utilised. Through exploring the data in these three periods, it is possible to distinguish inverse correlations between the number of COVID-19 cases/deaths as compared to the popularity and positive sentiment of Airbnb-related tweets. Other findings include the topics most mentioned along with Airbnb on Twitter and an illustration of how the 'voice' of COVID-19 manifests in Airbnb tweets. The unique contribution of this study is in exploring Twitter sentiment towards Airbnb throughout the pandemic, as well as after the vaccine dissemination.
查看更多>>摘要：Hashtags are popular navigability tools in a social media-driven environment. However, social media users have purposely employed a hashtag stuffing strategy, where many unrelated hashtags are added to a post to increase the visibility of the post and drive viewership. The results of the current study suggest a potential negative impact of hashtags on source trustworthiness assessment made by Instagram users through heuristic processing. This research conducted two experimental studies with samples from the overall Instagram population. Study 1 (N = 174) was a 2 × 2 between-subjects factorial experiment designed to demonstrate the positive effects of hashtags' navigability cues on Instagram users' perceived source trustworthiness. Study 2 (N = 185) was a 2 × 2 × 2 experiment that aimed to examine the interactive effect between the visual stimuli of a post and the heuristic cues of hashtags. The current research challenges some of the widely accepted hashtag strategies and provides several practical hashtag usage implications for social media influencers and companies.
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