The invention provides an artificial intelligence-based object pushing method and apparatus. The method comprises the steps of inputting historical click behaviors of a target user to a built user model for performing learning to obtain multi-dimensional preference eigenvectors of a target user; obtaining eigenvectors of all to-be-pushed objects; inputting the eigenvectors of the to-be-pushed objects and the multi-dimensional preference eigenvectors to a trained deep neural network model for performing prediction to obtain prediction probabilities of the to-be-pushed objects; and pushing the to-be-pushed objects to the target user according to the obtained prediction probabilities. According to the method and the apparatus, the preferences of the target user are obtained through the user model built by a neural network, and the probability of possibly purchasing to-be-pushed group orders by the target user is obtained based on the deep neural network and the preferences of the target user, so that the pushing is more effective; and preference features are selected through training by the user model, so that a large amount of manpower does not need to be consumed for selection and the pushing efficiency is improved.

Artificial intelligence-based object pushing method and apparatus

The invention provides a news recommendation system and method based on an FOLFM model. Based on a content recommendation method, a news content model is expressed abstractly through a latent class model and content characteristics, and an individual latent class preference model is built for each user. Real-time training is carried out on a real-time behavior record of a user to obtain preference, on certain latent class news, of the user, whether the news is recommended to the user is determined through calculation, and a final news recommendation list is obtained after a series of processing processes. The news recommendation system and method based on the FOLFM model deeply excavate the interest of the user, improve recommendation accuracy and satisfaction of the user, avoid a cold starting problem of the news, and guarantee performance under the condition that the recommendation effect is improved as much as possible. The experiment shows that the news recommendation system and method based on the FOLFM model not only guarantee the requirements for high accuracy and high speed, but also realize visual real-time recommendation for the user.

News recommendation system and method based on FOLFM model

The invention discloses a personalized research direction recommending system and method based on themes. Paper topics read by users and preference of the users for related paper topics can be obtained through the recommending system according to all the papers read by the users and according to the themes of the papers obtained when training is conducted through a theme model training module, therefore, the recommending system can recommend a new research direction for the users to widen the vision of the users. The innovation key of the personalized research direction recommending system and method based on the themes is to construct a three- layer graph model according to the relationship between the users and the papers and the relationship between the papers and the themes, to calculate preference values of the users for the themes according to the three-layer graph model, to obtain a user-theme preference weight matrix, and to calculate similar user set between the users and other users based on the weight matrix. The preference degree of the themes which are not touched by the users is predicted according to the similarity value of the similar users in the similar user set and according to the preference values of the similar users for the themes, and the research direction, namely, the research theme, is recommended for the users according to the prediction result.

Personalized research direction recommending system and method based on themes

The invention provides an individual service recommendation system based on a topic model. The individual service recommendation system comprises a social data acquisition module, a topic model construction and analysis module, a user interest modeling module, a service recommendation module, a user interaction module and a system management control module. According to the system, data is acquired by using a social network platform, the interests of a user are mined and modeled through the topic model, and the user interests are mined semantically, so that the problems of high dimension, data sparseness and the like of the conventional interest model based on a vector space model are solved; meanwhile, the long-term interests and short-term interests of the user are considered, the user interests can be reflected more truly, and accurate service recommendation specific to user interests is provided for the user.

Individual service recommendation system and method based on topic model

The invention relates to a news recommendation method and system, and relates to the technical field of information. The method and system aim to push news to different types of users who are interested in the news. The method includes the steps of extracting features of search and query data, according to behaviors of a certain type of users on the search and query data, calculating and recording interest weights of the features of the search and query data on the type of users, extracting features of multiple pieces of news to be pushed, searching for the interest weights of the features of the news to be pushed from the recorded features and the interest weights, calculating interest scores of the news to be pushed for the type of users, and pushing the news to the type of users according to the interest scores of the news to be pushed for the type of users. By means of the method and system, interests of different types of users can be analyzed, and the news according with the interests of the users is pushed to the users.

News recommendation method and system

The invention discloses a personalized news recommendation device and method based on news content and theme feature The recommendation device is equipped with seven modules, namely a news capturing module, a pre-treatment module, a theme model training module, a theme model predicting module, a user model building module, a news recommendation module and a recommendation treatment module The recommendation method comprises the following steps: building an personalized user model with the theme model and a relevant named entity noun sequence to express the interest preference of the user reading news, and calculating weight and converting the theme feature vector of users so as to reduce the influence of hot themes and single news content on the user interest, thereby effectively overcoming the defects of concentrated user interest and insufficient diversity of recommendation results In a recommendation output stage, an initial recommendation news list is treated, a theme grouping process based on the personalized user model is added on the basis of currently repeating data deleting and redundancy filtering, and news texts are reordered again according to the aging weight so as to recommend the accurate, diversified and novel personalized news

Personalized news recommendation device and method based on news content and theme feature

The invention discloses a multidimensional-similarity-based personalized news recommendation method. The method comprises the following steps of: extracting a set time record from a news log, capturing news and extracting a title and a text according to a news source address, performing word segmentation and noun extraction on the title and the text, and analyzing a noun sequence by using a subject model to acquire a subject feature character of the news; constructing a user model and a news model respectively according to the subject feature vector of the news and user behavior data; computing the content similarity and behavior similarity of users and the news respectively according to the user model, the news model and a time feature, computing final user similarity and final news similarity according to the content similarity and behavior similarity of the users and the news, and extracting a plurality of users and news which are most similar respectively; and generating user-based personalized recommendation results according to a latest news log record and a plurality of similar users which are most similar to a set user; or generating news-based personalized recommendation results according to the news on which the set user behaves and the news which is most similar to the news.

Cao Yiming

Papers

Personalized news recommendation device and method based on news content and theme feature

Multidimensional-similarity-based personalized news recommendation method

Text grouped clustering method based on content characteristic and subject characteristic