30results about How to "Intelligent Recommendation" patented technology

The invention discloses a method and device for recommending software upgrading. The method can comprise the steps that the use parameters and / or upgrading quantity white list of software in a user terminal are / is acquired; the use parameters comprise software use frequency and / or software upgrading frequency; a list of the software to be upgraded is generated according to the use parameters and / or the upgrading quantity white list; the list of the software to be upgraded is matched with a local software library to generate the soft upgrading recommending list of the user terminal. The upgrading intention strength of the user terminal on different software is judged through the local software use frequency of the user terminal and the frequency of upgrading the software through a software management client side, and therefore the software capable of being upgraded is actively recommended to the user terminal more precisely and more intelligently.

The present invention relates to the technical field of recommendation systems, in particular to a personalized intelligent clothing collocation recommendation method combined with knowledge graphs, which acquires clothing product information on an e-commerce platform and user clothing interaction information and constructs a knowledge graph; models the knowledge graph, and Each entity and relationship in the knowledge map is learned to obtain entity vectors and relationship vectors; the entity vector and relationship vector are combined with a hybrid recommendation system to predict the rating of single-item clothing and obtain clothing single-item recommendation results; calculate pairwise The matching index of clothing items, automatically match two clothing items with high matching index and predict the score, and make the final TOP-N suit recommendation result according to the score. This method makes online clothing suit recommendation more intelligent.

The invention relates to the technical field of intelligent decision-making, and provides a parking space navigation method, an electronic device and a storage medium. This method performs grayscale processing on the collected image of each parking space, extracts the contour image of each parking space, and extracts the Haar feature of each contour image to train a preset model to obtain a vehicle recognition classifier. Afterwards, the Haar feature corresponding to the real-time image of the parking space captured at a preset time point is input into the vehicle recognition classifier to identify the empty parking space in the parking lot, and generate a parking space according to the empty parking space and the current position of the vehicle. A route is recommended to the client of the user. The invention can make the parking lot users know the vacant parking spaces in the parking lot at a glance, find the parking spaces quickly, recommend the best parking route to the users more intelligently and accurately, and avoid parking difficulties and waste of time for the users due to unfamiliar parking conditions. situation, while improving the efficiency of parking operations in the parking lot.

The present invention provides a method for recommending clothing and a mobile terminal. The method includes: acquiring user's schedule information; target dress style label; determine N candidate dress images marked with the target dress style tag from the target dress library, where N is a positive integer greater than or equal to 2; A target dress image matching the pre-acquired dress reference factors; displaying the target dress image. The dressing recommendation method provided by the present invention can recommend dressing images to the user very intelligently, so that the user can solve the dressing problem conveniently and quickly.

The invention provides an intelligent recommendation method for review experts oriented to scientific and technological projects. The present invention comprises the following steps: 1). Segment the main text of pending scientific and technological projects and expert information into substring sequences and perform word segmentation by ICTCLAS of the Chinese Academy of Sciences, and perform stop word filtering on the word segmentation results to obtain a word set; 2). Build the words of project information Network, extracting feature words based on statistical features and aggregation features; expert information is relatively streamlined, and the word set obtained in step 1 is directly used as feature words; 3). Construct a knowledge representation model based on the fields and weights of feature words; establish relevant information indexes; 4 ). The group recommendation experts perform inter-field and inter-item feature merging operations on the knowledge representation model; 5). Based on the similarity between the semantic calculation experts and the technology projects (groups) to be reviewed, a threshold value is set to truncate to generate the final recommendation expert list. The implementation of the above method can greatly alleviate the problems of heavy workload in recommendation and lack of scientific review decision-making.

The invention discloses a real-time goods recommendation method based on customer shopping intention exploration. The method comprises the following steps: 1, constructing a tree-shaped customer shopping intention model; 2, detecting user behaviors in real time and identifying user intention; 3, detecting the change of the user intention in real time; 4, tracking the change track of the user intention and predicting the deep intention of the user: predicting the deep intention of the user based on the discrete intention of the user; and 5, determining a goods recommendation strategy: determining the goods recommendation strategy according to the shopping intention of the customer. The method disclosed by the invention automatically identifies the shopping intention of the customer, performs precise individualized recommendation, and improves the customer satisfaction; and moreover, the method automatically detects the change of the shopping intention of the customer and adjusts the goods recommendation strategy. The method adapts to the changing interest of the customer in real time, deeply explores the shopping intention of the customer based on the user intention model and provides professional recommendation service to the user.