The emergence of mobile wireless technology has profoundly altered how people produce goods and live their lives, but even as we take advantage of its speed and convenience, we must also be aware of its drawbacks. A new technology supported by contemporary technology is mobile wireless Internet technology. It is computer-, network-, and new media-based. Education and teaching are becoming more important in society as a result of the social economy’s rapid growth and the reform and development policies’ gradual advancement. It can develop the skills required by society and make teaching more widely known. The semisupervised learning positioning algorithm used in this study lowers the training phase acquisition costs. The proposed algorithm significantly resolves the issue that the DV-Hop algorithm is not suitable for anisotropic network positioning by applying the concept of triple equidistant coverage to anisotropic wireless sensor networks and combining it with multiple mobile beacons. According to the results of the experimental analysis, the following conclusions can be made: information processing can be found to be essentially stable at a level of more than 40%, which will greatly strengthen the use of mobile wireless technology in higher education. The improvement in overall performance will reach 65.7 percent. Following the experiment, it was discovered that the average transmission efficiency for mobile wireless technology is 57.4%, which is extremely high.
The factors that influence how people play mobile games have been studied from a variety of perspectives in the wireless broadband environment. The original data in the background of the game, such as user operation records, consumption records, and social behavior records, are converted into user attributes, user tags are generated, and data sets are constructed in this study, which primarily uses data mining technology to study user behavior and form user portraits. By incorporating the similarity of players’ subspace interests into the CFR (collaborative filtering recommendation) algorithm, a personalized game recommendation model, as well as the relationship management level of mobile game players, is created. The final fusion model’s ROC-AUC value is 0.921, which has a percentile enhancement effect, according to the results. The findings show that using a personalized game recommendation model can help to improve the scalability of the CFR algorithm and the impact of data scarcity on the quality of mobile games recommended by players.
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