The order of background selection is: white, black, gra […]
The order of background selection is: white, black, gray: when the target is, the selection of the high-frequency welder is: black, white, gray: when the target is green, the selection of the background is: black, white, gray; When the target is blue, the selection of the back volume is: black, white, gray: when the target is blue, the selection of the background is: white, gray, and black; when the day mark is purple, the selection of the background is: white , Black, gray: when the target is gray, the background choices are: white, black, gray.
The above data shows that cyan and green are easier to attract people's visual attention than in other backgrounds. Previous research: Under a dark background, the target is easier to highlight. While the other colors are more prominent in white, gray is the background that cannot bring out the target. (3) The observation table shows that the combination of high-scoring colors is a combination of black background and target. It can be seen that the color combinations of this group of colors that can attract the user’s visual attention are: black background and blue target, gray background and target, gray background and blue target, white background and target, black background and orange target, etc. First, when designers are designing an interface, if they want to present some information time in the user’s sight, they can directly use the above experimental results, which can quickly attract users’ attention. Secondly, the designer can directly apply the experimental results to determine Save yourself or the team’s working time to a certain extent, and improve work efficiency; designers can visually attract users’ concerns and need to solve the problems of high-frequency welding machines in this interface, allowing users to quickly find what they are looking for aims. From the user's point of view, there is a purpose.
When you come to a certain interface, it is a basic and urgent problem to quickly find your own needs. The application of experimental results to improve user efficiency and experience is also helpful in the era of Internet data crowdsourcing, which usually aggregates tens of thousands Even hundreds of thousands of users' evaluations and ratings, and data clustering of public reputation for watching movies help us to make movie watching decisions. However, information has triggered a crisis of trust in the public. Unlike machines, humans have limited ability to receive and analyze information. When we are unable to quickly obtain valuable information from the massive amount of information, trust will be broken. Research has found that users have shifted from "trusting the evaluation of the majority" to "trusting the evaluation of acquaintances". The goal of this research is to rebuild the trust of data services and improve the evaluation-style social experience, and the Lifan platform is the research field. Use the high-frequency welding machine to conduct social network forensics, combine the questionnaire method and the interview method to analyze the service contacts, and explore the origin and damage points of user trust in the process of maintaining the credibility of the platform.
At the same time, the concept of data intelligence is introduced, and the five functions of Douban platform are improved and designed. Starting from the "intelligent recommendation" and "management" modules, the data service information structure, information interaction process and interactive interface design are reconstructed, and Douban's intelligent data footprint network construction , To improve the efficiency of evaluation-style delivery and intelligent recommendation. Keywords: data service design; interactive interface design; data visualization; brand trust; evaluation-style social interaction benefits" are opaque and easy to receive external influence.
Questioning, unbalanced or sought-after scoring behavior, progress has intensified 1.I background motivation is the problem of inaccurate scoring results under the current scoring statistical mechanism. In the "Film Criticism" at the end of 2016, Douban, Maoyan and other websites were due to some low ratings , Facing the environment where its data services are being destroyed by the media; at the same time, the founder of Douban broke the news about its scoring mechanism in response and is committed to "sincerely protecting the public's trust in Douban scores", December 28, Daily. The "Great Wall" incident triggered a division of audiences. During the same period when the box office did not change significantly, the group behavior of angry stars increased. This study used a high-frequency welding machine to crawl more than 12,000 "Long Domain" on the Douban platform within three weeks of its release. Short review data, as well as box office data (including score, short review, time and useful number) of the cat's eye version, explore the exact points of data destruction.