9 results about "Internet users" patented technology

To provide an information processing system and method for reducing labor and time when manually monitoring contribution information contributed to a plurality of contribution sites.SOLUTION: In the information processing system, a management server 10 includes an acquisition unit 11 that acquires posted information corresponding to a specific object from each of a plurality of posting sites (posting site servers) that disclose posted information posted by users who have used the specific object on the Internet, and an output unit 12 that outputs the posted information corresponding to the specific object acquired from each of the plurality of posting sites to be displayed in a list.SELECTED DRAWING: Figure 2

The invention provides a quasi-real-time dynamic IP blacklist control method, a storage medium and electronic equipment. The method comprises the following steps: collecting log data accessed by Internet users based on a distributed message queue; acquiring user access feature data for the log data accessed by the internet user based on a real-time analysis database; judging whether an abnormal IP identification process is triggered based on the user access feature data, performing abnormal IP risk assessment based on the user access feature data, a plurality of preset abnormal IP matching factors and a priority sequence of the matching factors when the abnormal IP identification process is triggered, and determining a blacklist IP list according to a result of the abnormal IP risk assessment; and synchronizing the blacklist IP list to a cache service, and intercepting the IP in the blacklist IP list based on interaction between a web service and the cache service. According to the invention, the real-time performance and accuracy of IP blacklist protection can be effectively improved.

This invention belongs to the field of internet user preference prediction technology, specifically relating to a user preference prediction method based on data debiasing. The method includes: acquiring video data and user data; processing the video data and user data using an embedding layer to obtain user embeddings and video embeddings; debiasing the user embeddings and video embeddings to obtain debiased user embeddings and debiased video embeddings; processing the user embeddings, video embeddings, debiased user embeddings, and debiased video embeddings using a feature filtering layer to obtain user-specific feature importance weights and video-specific feature importance weights; processing the user-specific feature importance weights and video-specific feature importance weights using a factorization machine to obtain a user interest representation vector; and inputting the user interest representation vector into an output layer for processing to obtain the user preference prediction result. This invention can accurately and effectively predict user preferences, improving the accuracy of user preference prediction.

This invention discloses an intranet traversal system, comprising a client and a server. The client runs on a computer within a local area network (LAN), and the server runs on a cloud host. Each client connects to an internal service within the LAN. When the corresponding internal service requires traversal, the client initiates a connection request to the server. The server receives the connection request from the client, establishes a tunnel with the client, and starts a proxy service for the internal service corresponding to that client. The proxy service receives access requests from internet users and forwards them to the internal service within the LAN via the server and client. This invention provides strong computing and storage services while controlling costs and simplifying intranet traversal operations.

This invention belongs to the field of internet user access experience technology, specifically relating to an internet access quality monitoring system and method for real-time perception of user experience. This invention constructs a diffusion intensity index by using the adjacency matrix change rate and the experience quality index change rate. This index can identify whether network quality anomalies are propagating in the spatial structure, achieving an improvement from single-point anomaly monitoring to group trend monitoring. The network performance sampling frequency is dynamically adjusted according to the diffusion intensity, improving sampling accuracy during anomaly propagation and reducing sampling load in stable conditions. This reduces system resource consumption while ensuring monitoring accuracy. By integrating individual network performance, spatial adjacency relationships, time window fluctuation statistics, and diffusion trend judgment, real-time dynamic perception and adaptive monitoring and control of user experience quality are achieved, resulting in significant improvements in anomaly identification accuracy, group diffusion perception capability, and system resource utilization efficiency.

A method and system for authenticating an Internet user identity by cross-referencing and comparing at least two independent sources of information. A first geographical location of computer signature of an internet user is identified and the geographical location of a communication voice device of said internet user is identified to determine a second location. The first and second locations are compared for geographical proximity to confirm the identity of the internet user. Based upon geographical proximity of said locations, a score is assigned to the internet user, and access to a website is allowed or limited based upon said score. Alternatively, additional authentication information can be required or access can be terminated.

A method for authenticating the identity of a user who is attempting to access a website or conduct a transaction is described. The method involves receiving two geographical locations, and associated time stamps, of a mobile phone associated with the user, one of the locations being the location of the transaction or access attempt. The method determines whether the speed required to travel between the two geographical locations in the elapsed time is within acceptable limits. A confidence score, derived in part from this calculation, is taken into account when deciding whether to allow or deny the access or the transaction.

Watermarking media content, in combination with blockchain and distributed storage networks, prevents the proliferation of Deepfake content. Digital watermarks are embedded in the audio and video tracks of video clips of trusted content producers at the time the videos are captured or before they are distributed. The watermarks are detected at the social media network's portals, nodes, and back ends. The embedded watermark imparts a unique identifier to the video, that links it to a blockchain. The watermarks also allow video source tracking, integrity verification, and alteration localization. The watermark detectors can be standalone software applications, or they can be integrated with other applications. They are used to perform three main tasks: (1) they alert the Internet user when he watches an inauthentic news video, so that he may discard it, (2) they prevent a Deepfake content from propagating through the network (3) they perform forensic analysis to help track and remove Deepfake content postings.

This invention relates to a method for predicting internet user ad clicks based on big data, belonging to the field of internet advertising technology. It aims to solve the problems of difficulty in modeling high-dimensional sparse features, insufficient capture of feature interactions, and weak prediction generalization ability in existing technologies. The method first acquires user features, ad features, and historical behavior sequences. User and ad features are divided into coarse-grained and fine-grained categories and encoded. Low-dimensional dense features are extracted through feature processing. Next, the user's historical behavior sequences are embedded and sequence modeled to obtain dynamic interest vectors. Subsequently, the dynamic interest vectors are concatenated with the coarse-grained low-dimensional features, and dynamic sample clusters are obtained through clustering. A dedicated model is built for each cluster, fusing fine-grained features. Finally, the fused features are input into a deep neural network containing both click and deep interaction tasks, outputting the prediction results. This invention, through layered processing of coarse and fine-grained features, clustering and grouping modeling, and dual-task collaborative optimization, effectively mines deep feature correlations, accurately captures user dynamic interests, and significantly improves the accuracy and generalization ability of click prediction, providing reliable support for precise ad targeting.