The invention provides an information recommendation method and device. After information to be recommended is obtained, information characteristic vectors of the information to be recommended are extracted, meanwhile user characteristic vectors of a user to be recommended are obtained; then, a well-trained recommended algorithm model based on a factorization mechanism is utilized to conduct fitting on the information characteristic vectors of the information to be recommended and the user characteristic vectors, user information matched with the information to be recommended is obtained, namely a user group possibly interested in the information to be recommended is obtained. Due to the fact that the well-trained recommended algorithm model based on the factorization mechanism gives consideration to the association of the characteristics during calculation, recommendation results of the information recommendation method are more accurate.

Information recommendation method and device

The invention provides an advertisement click-through rate (CTR) prediction method based on a factorization machine (FM). The method includes: carrying out analysis and abnormal-value elimination on click record data; encoding feature data; training the FFM (Field-aware Factorization Machine); and predicting a click-through rate. According to the method, an FFM model is adopted to carry out combined classification on features, and thus accuracy of advertisement click prediction is improved.

Advertisement click-through rate (CTR) prediction method based on factorization machine (FM)

The embodiment of the invention provides a method for portraying a user portrait. The method includes the steps that for a target user, original data, used for portraying the target user, of the userportrait is obtained; based on the original data, user features of the target user on multiple first preset dimensions are determined; according to the expressed user features of the target user on the first preset dimensions, the user portrait of the target user is portrayed. With the method, the defect that an existing user portrait only reflects user features in specific fields and can not reflect user features in other fields is overcome, and therefore the application field of portraying is obviously expanded for the user portrait. In addition, the embodiment of the invention provides a device for portraying the user portrait, a computer readable storage medium and calculating equipment.

Method and device for portraying user portrait, medium and calculating equipment

The invention provides a semantic computing method for improving a word vector model. The method comprises the following steps of S1, preprocessing a corpus; S2, tagging parts of speech and tagging the parts of speech of words obtained through preprocessing of the corpus; S3, initializing vectors and vectoring the words obtained through tagging of the parts of speech and the parts of speech; S4, integrating context vectors and computing and integrating context word vectors and part of speed vectors of the words; S5, establishing a Huffman tree, training a network, optimizing a target function and judging whether an error reaches a threshold value or not; S6, obtaining the vectors and obtaining the word vectors and the part of speed vectors; and S7, applying the vectors and carrying out semantic computing through application of the word vectors and the part of speed vectors. Compared with the prior art, the method has the advantages that a part of speech factor is added to the vectors, an existing Word2vec model is improved, moreover, innovative application is carried out according to the improved model, and a function of carrying out semantic computing through the Word2vec is expanded.

Semantic computing method for improving word vector model

The invention discloses an information recommending method and system. The information recommending method comprises the steps of obtaining an inherent attribute and a behavior attribute of a user; performing statistical analysis treatment on the inherent attribute and the behavior attribute, and excavating user interests; performing analysis excavation treatment on the behavior attribute, and excavating the reading habit and the consumption concept of the user; according to the inherent attribute, the behavior attribute, the user interests, the reading habit and the consumption concept, excavating a user image and behavioral habits of the user; according to the user image and the behavioral habits, calling a preset information recommending model to obtain recommended information, and pushing the recommended information to the user. According to the information recommending method, various pieces of preference information of the user can be excavated, diversification of the recommendedinformation is achieved, and the requirement for extensive reading of the user is met.

Information recommending method and system

The invention provides a game recommending method and system based on user portrait behavior analysis. A feature acquirer is constructed to process user portrait data, application list data and client-reported data to obtain regular feature vectors meeting mathematical modeling requirements; various basic recommending models are used to make predictions to generate a primary user application recommendation list and corresponding download probabilities; a final application recommendation list is generated by combining the download probabilities and an actual label training fusion model. User historical behavior logs are subjected to multidimensional analysis to perform feature extraction so as to construct a user portrait data warehouse. A long and short-term memory network is introduced to the basic recommending models to learn time-series relationships of user behaviors, users' degrees of preference for objects are better depicted, and recommended game applications match well with the needs of users. Integrated learning is added to perform model fusion, learning results of the models are integrated, and accordingly, the stability and generalization ability of a recommending algorithm are improved.

Application recommending method and system based on user portrait behavior analysis, storage medium and computer device

The invention provides an advertisement information pushing method. The method comprises the steps that a server side acquires the advertisement pushing request of a client; the server side sends an advertisement request to an advertisement pool module, and the advertisement pool module acquires advertisements from a database; the server side receives the advertisements returned by the advertisement pool module; the server side calls an algorithm policy module to estimate the click rate of the advertisement data, and carries out filtering and sorting; and the server side sends filtered and sorted advertisements back to the client. Compared with the prior art, the method provided by the invention carries out personalized advertisement recommendation for mobile device users in view of the characteristics of a mobile terminal. According to the invention, more suitable advertisement data can be more accurately selected for the needs of users, and are accordingly pushed to users. The invention further provides an advertisement information pushing system for implementing the method.

Advertisement information pushing method and system

The invention relates to a network data collection, storage and processing method The method comprises the following steps: S1, collecting network data; S2, storing the collected data; and S3, processing the stored data The step S1 specifically includes: S11, carrying out storage management analysis on URLs; S12, carrying out automated task scheduling; S13, generating parallel crawling tasks, and saving captured HTML files; and S14, parsing an HTML document tree, and extracting needed field information The invention also provides a network data collection, storage and processing system used for realizing the above-mentioned method Compared with the prior art, the method and the system can realize collection of the valuable data in network information, and extracting the structured information through offline document parsing, and are better than a prior network data collection device in collection efficiency and stability

Network data collection, storage and processing method and device

The invention relates to a message filtering method. A Bayes classifier model is used for calculating the probability that a message is a rogue message; the property of the message is judged according to the probability of the rogue message, wherein the property of the message includes the normal message, the rogue message and the suspicious message; the rogue message is directly filtered away, and the normal message and the rogue message are remained; then, the further classification is performed by using a semanticbased deep study model; and the message is determined to be the normal message or the rogue message. Compared with the prior art, the method and the device have the advantages that the automatic study can be realized; a training set is regularly updated; the synonym is recognized; the manual annotation cost is greatly reduced; and high accuracy, robustness and stability are realized. In addition, the invention also provides a message filtering realization device.

Message filtering method and device

The invention relates to a data recommendation method based on face recognition, a device, a server end and a client end. Acquiring a stored face feature vector corresponding to the user identification according to the user identification; A human face feature vector is extracted from a human face image, whether the extracted human face feature vector matches the stored human face feature vector is judged, and if the extracted human face feature vector matches, recommendation data corresponding to the stored human face feature vector is obtained, and the recommendation data is sent to a smallprogram for display. By using face recognition as the basis of data recommendation, Convenient to use, effectively prevent information leakage problem, can better portray the user portrait, through the combination of small procedures for data recommendation, without installation and uninstallation application, can achieve rapid data analysis and processing, reduce the occupation of resources, reduce the development cost and development threshold, can be widely used in life service scenarios.

Yin Jian

Papers

Application recommending method and system based on user portrait behavior analysis, storage medium and computer device

Advertisement information pushing method and system

Network data collection, storage and processing method and device

Message filtering method and device

Face-recognition-based data recommendation method, device, server end and client end