The invention discloses a cloud computing-based intelligent perception recommendation method. The effect problem of a personalized recommendation service is researched, and from intelligent perception, based on a Hadoop framework, a cloud computing-based intelligent perception recommendation system is proposed. According to the method, distributed computing is realized through the Hadoop framework; users are effectively clustered before user similarity calculation; user similarity values are calculated by adopting an improved similarity calculation method; and finally a score of a target user is predicted and a recommendation result is given. Through the method, the accuracy and diversity of the personalized recommendation result are scientifically and reasonably improved. The method is suitable for various enterprises which already develop e-commerce activities and pursue for high-quality personalized services.

Cloud computing-based intelligent perception recommendation method

The invention discloses a behavior data processing method and device and electronic equipment. The method comprises the steps that by counting a plurality of behavior data of a specified user on a specified object type in a first time period through statistics, the weights of the behavior data of the specified object type are obtained; according to the behavior data and the weight of each behaviordata, the behavior characteristic value of a preset behavior of the specified user on the specified object type is calculated, so that the behavior of the specified user is predicted, and the behavior data can be finely processed by means of the weight of the behavior data; moreover, the behavior of the user can be predicted through the weight of the behavior data and the behavior data.

Behavior data processing method and device and electronic equipment

The invention discloses a personalized recommendation method based on clustering PU matrix decomposition, which comprises steps: a clustering PU matrix decomposition method is used to train a model; a user or a goods is recommended, existing user or goods feature data are firstly acquired, a binary relationship matrix is built according to behaviors of the user to the goods, only positive examples in the binary relationship matrix can be observed, a learning model based on the clustering PU matrix decomposition method is defined, and a recommendation algorithm model is trained; and finally, in the case of recommendation, the model obtained by training is used to predict the relationship between the user and the goods, prediction values are obtained, and according to the prediction values from high to low, the user or the goods is recommended.

Personalized recommendation method based on clustering PU matrix decomposition

The invention discloses a method and a system for determining user preference, a readable storage medium and an electronic device. The method comprises the following steps: determining the number of service types corresponding to the service types of each level in a user access classification system, and constructing a criterion matrix according to the service types and the corresponding service type numbers; calculating a first weight value corresponding to each service type according to the criterion matrix; constructing a user behavior matrix, and calculating according to the user behaviormatrix and the first weight value of the corresponding level to obtain a user weighted behavior matrix; calculating according to the user weighted behavior matrix to obtain a second weight value corresponding to each service type, and confirming the service type preferred by the user according to the second weight value. The method for determining the user preference proposed by the invention canaccurately determine the service type preferred by the user, and meets the actual analysis requirement.

Method and system for determining user preference, readable storage medium and electronic device

The invention relates to a scientific and technological information resource retrieval inquiring system based on big data. The system comprises the first step of acquiring user data and uploading the user data to a server; the second step of intercepting, analyzing and processing the data uploaded by a client side, and feeding the analyzing result back to the server; the third step of conducting preprocessing and aggregation processing on the user data, and storing useful intact data into the big data; the fourth step of conducting behavioral analysis and extracting behavioral information which a user frequently browses; the fifth step of conducting data retrieval on a scientific and technological information resource relevant to user behaviors; the sixth step of pushing data to the user; the seventh step of conducting statistics and analysis to form a user behavioral analysis statement and pushing the statement to the user. According to the scientific and technological information resource retrieval inquiring system based on the big data, technologies like cloud computing, the big data and a search engine are comprehensively utilized, on the premise that the safety is guaranteed, professional, effective and accurate pushing of a large amount of user real-time behavioral data which is collected in real time is achieved, and thus the user demand is satisfied.

Scientific and technological information resource retrieval inquiring system based on big data

The invention discloses a Hadoop cloud platform-based Web resource personalized recommendation system and method. The system comprises a user model module which collects browsing behavior information of a user on a Web resource page, endows different weights to different behavior types of the user according to the browsing behavior information of the user on the Web resource page, and calculates the degree of interest of the user in the Web resource page according to the different weights, a recommendation algorithm module which carries out clustering processing on the collected browsing behavior information of the user on the Web resource page by using a recommendation algorithm according to the degree of interest calculated by the user model module, then constructs a matrix according to a clustering result, carries out nearest neighbor analysis on a target resource through the constructed matrix to obtain a nearest neighbor result of the target resource, and forecasts and fuses the degree of interest of the user according to the nearest neighbor result to obtain a recommendation scheme, and a parallel processing module which carries out off-line distributed calculation on the recommendation algorithm under a Hadoop cloud platform, and saving a calculation result.

Hadoop cloud platform-based Web resource personalized recommendation system and method

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Hadoop cloud platform-based Web resource personalized recommendation system and method