30 results about "Cold start recommendation" patented technology

The invention relates to an App cold start-up recommending method based on a mobile app operation list and mainly relates to the fields of information retrieval and system recommendation. The method comprises the following steps of acquiring large number of app information and arranging keywords, calculating a weighing undirected graph indicating keyword relationships, and calculating and pushing the weighing undirected graph according to to-be-pushed App information. After training data is trained to form an initial model, the model can greatly recommend according to the operation list content and the cold start-up problem can be well solved; the App cold start-up recommending method has great extendibility; and deficiencies of content recommendation can be overcome to some extent.

A recommendation system based on collaboration, including: an edge system module, a collaboration center system module, and a target system module, and the edge system module, the collaboration center system module, and the target system module are connected in sequence; the edge system module collects and stores user information and sends it to the collaboration center The system module provides the required specific user information; the cooperation center system module responds to the cooperation request of the target system module, sends a cooperation request to the edge system module, and performs inconsistency elimination and reasoning fusion on the user information, and outputs the required information to the target system module. Specific user data; the target system module provides a personalized recommendation service to the user, and initiates a collaboration request to the collaboration center system module during a cold start. The present invention adds a user cooperation mechanism to the recommendation system, and the cooperation center system module completes the cooperation function between multiple recommendation systems, performs fusion processing on the multi-source data of a specific user, and effectively solves the cold start problem in the recommendation system.

The embodiment of the present application belongs to the field of big data technology, and relates to a cold start recommendation method and related equipment, which can be applied to the field of smart security, including: when receiving the first key sent by the first server, calculating the first key of the local server Scoring parameters; when receiving the first encrypted data sent by the second server, calculate the first encrypted similarity according to the first scoring parameter and the first encrypted data, and decrypt the first encrypted similarity according to the first key to obtain Decrypting the first similarity; sending the first decrypted similarity to the first server; when receiving the total similarity sent by the first server, generating an item recommendation table for the target user according to the total similarity. In addition, the present application also relates to blockchain technology, and the item recommendation table can be stored in the blockchain. This application realizes the protection of user privacy information during cold start recommendation.

The invention provides a hidden factor model weapon task recommendation method with feature vectors. According to the technical scheme, the method is used for solving the problem of information overload appearing in a Weiguan platform and recommending suitable tasks to users, and mainly comprises the following steps: firstly, carrying out user interest degree quantification and feature set construction, quantifying original behavior data, reading the original behavior data into a feature set, and introducing negative sampling to enrich the original behavior set; then establishing a hidden factor model with a correction vector, carrying out training, and generating a recommendation result; and finally, for users and tasks which newly enter and do not have behavior information, providing cold start recommendation based on the correction vector group. According to the characteristics of the Weiguan platform data, the user characteristics and the task characteristics correspond to the correction vectors and are introduced into the hidden factor model, more accurate modeling is carried out on the interest of the user, meanwhile, the cold starting problem when a new user and a new task enter is solved by utilizing the user characteristic vectors and the task characteristic vectors in the model, and the practicability is high.