Student Attitude to Internet Search Engines: Navigation and Optimization Problems

The article relevance. Currently, the world is rapidly undergoing the process of Informatization of all aspects of society, the development and introduction of new information technologies. This highlights the need for further reflection and research on the development of the Internet and its opening opportunities for people. The aim of the research is to study the peculiarities of the attitude of students to search and recommendation services on the Internet. Research methods: as a research method, we used a questionnaire survey as a method of collecting primary information, which allows us to identify various aspects related to the attitude of students to search and recommendation systems on the Internet. Research results: the article examines the specifics of Russian search and recommendation systems, students' attitude to these services, and their place in their lives. The novelty and originality of the research lies in the fact that for the first time the search and recommendation services of the Internet space were studied. It is shown that these services were initially developed in the sphere of culture and gradually began to spread to other spheres of people's life, which attracted e-Commerce figures. It is revealed that those students who discovered search and recommendation services a few years ago still use them to choose leisure activities. It is shown that students still identify some disadvantages of these systems: inaccurate recommendations, a large number of questions to determine preferences. There is some distrust to new Internet technologies among those who are used to relying on their intuition when choosing. It is determined that students often use search and recommendation services, since in most cases gadgets help them spend their free time, have fun: read a book, watch a movie, listen to music. It is revealed that the majority of students trust Internet services, although they are not always satisfied with the recommendations. It is shown that the level of student-user confidence in traditional advertising and marketing decreases simultaneously. It is determined that from the point of view of students, today not only printed versions of Newspapers, traditional radio, but even mass broadcast television are losing ground before the Internet as the most promising communication channel. Practical significance: the data Obtained in this work can be used in marketing research, economic Sciences, advertising psychology, as well as for further theoretical development of this issue.

Place Classification Algorithm Based on Semantic Segmented Objects

Scene or place classification is one of the important problems in image and video search and recommendation systems. Humans can understand the scene they are located, but it is difficult for machines to do it. Considering a scene image which has several objects, humans recognize the scene based on these objects, especially background objects. According to this observation, we propose an efficient scene classification algorithm for three different classes by detecting objects in the scene. We use pre-trained semantic segmentation model to extract objects from an image. After that, we construct a weight matrix to determine a scene class better. Finally, we classify an image into one of three scene classes (i.e., indoor, nature, city) by using the designed weighting matrix. The performance of our scheme outperforms several classification methods using convolutional neural networks (CNNs), such as VGG, Inception, ResNet, ResNeXt, Wide-ResNet, DenseNet, and MnasNet. The proposed model achieves 90.8% of verification accuracy and improves over 2.8% of the accuracy when comparing to the existing CNN-based methods.