30 results about "Cold start recommendation" patented technology

The invention discloses a cold-start recommendation method based on user preferences and trust. The method comprises the steps of S1, measuring comprehensive trust values between users according to social information of the users, and constructing a trust relation matrix; S2, calculating preference similarity degrees of the users according to user scoring data, and constructing a preference relation matrix; S3, utilizing a calculation method of comprehensive similarity degrees to fuse preference relations and trust relations, and using a bee colony algorithm to iteratively update weights in the comprehensive similarity degrees, carrying out multi-objective optimization to enable the weights to become optimal in a self-adaptive manner, and constructing a preference trust relation matrix; S4, selecting a most-trusted neighbour set of the target user to predict scoring values of corresponding items for the target user on the basis of the preference trust relation matrix; and S5, recommending the items with high prediction scores to the target user. According to the method, the precision of user trust measuring is improved, the user behavior preferences are more accurately constructed, and the quality of recommendation for the cold-start user is improved.

The invention relates to a social information enhancement-based event social network recommendation algorithm. According to the algorithm, the cold-start recommendation problem of event recommendationis solved by trying to combine information of a conventional social network and an event social network. Firstly, by utilizing social information of an event organizer, an event organizer assessmentalgorithm is introduced, thereby enhancing the effect of the recommendation algorithm. Meanwhile, the call guide effect of event participants is calculated, and the influence effect of the participants on a user is quantized and serves as a factor of a recommendation event. Secondly, the influence effects of the organizer and the participants of the event, and information in various aspects such as time, place, contents, groups and the like of the event are integrated, and selection preferences of the user are found. Finally, in combination with a search sorting algorithm, a recommendation list is calculated for each user, thereby realizing personalized recommendation.

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 invention relates to the technical field of information recommendation, and discloses a method for information recommendation. The method comprises the following steps: acquiring personal characteristic information of a user, label information of the user, context information of the user and social network information of the user; obtaining a first alternative recommendation list according to the personal feature information, obtaining a second alternative recommendation list according to the tag information, obtaining a third alternative recommendation list according to the context information, and obtaining a fourth alternative recommendation list according to the social network information; extracting to-be-recommended information from the first alternative recommendation list, the second alternative recommendation list, the third alternative recommendation list and the fourth alternative recommendation list according to a preset extraction rule, and combining the extracted to-be-recommended information to obtain a cold start recommendation list; and recommending the to-be-recommended information in the cold start recommendation list to the user. Therefore, accuracy of recommending information to a new user can be improved. The invention further discloses a device for information recommendation, electronic equipment and a storage medium.

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.