88 results about "Search history" patented technology

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating descriptions of digital components. In one aspect, a method includes receiving data indicating a query received from a client device of a user. An initial digital component is obtained. Search history data that includes a set of related past queries received from the user is obtained. Updated text related to the first resource is generated by conditioning a language model with one or more contextual inputs that cause the language model to generate one or more outputs that include the updated text, the one or more contextual inputs characterizing one or more of the first query, data related to the initial digital component, the sequence of related past queries, or one or more tasks to be performed by the language model. An updated digital component that depicts the updated text is generated and provided.

Techniques for generating and presenting search filter recommendations are disclosed. A system determines a composition of search filter tiles, including a relative number of machine learning-generated search filter tiles and non-machine learning-generated search filter tiles, based on recommendation criteria. The recommendation criteria include an amount of user search history data that exists. The system applies search filter recommendation policies to generate a set of search filter recommendations, including hard-coded search filters, search filter recommendations generated by a user-specific machine learning model, search filter recommendations generated by a machine learning model trained on data from multiple different users, and algorithm-based recommendations. The system generates different sets of search filter recommendations made up of different ratios of recommendations from different sources based on determining if search filter criteria are met.

An e-commerce precise advertisement putting method based on big data mining comprises the following steps: collecting user data: collecting multi-dimensional data of a user through an e-commerce platform, including but not limited to browsing history, search history, purchase record, social media interaction, geographical location information and the like; all data should be encrypted and desensitized to ensure data privacy security, modeling is performed on features such as interest, purchasing power and purchasing cycle of a user based on user data by applying clustering analysis, association rule mining and other big data analysis technologies, a dynamically updated user portrait is formed, and a personalized user tag is established on the basis, so that the user experience is improved. , such as price sensitivity, product preference and the like. A differential privacy technology is adopted to protect user data, and the personal privacy of a user is ensured not to be leaked through a noise injection and encryption algorithm. Even in the big data analysis process, the sensitive information of the user data cannot be restored, so that the trust of the user to the platform is greatly enhanced, and the risk of data leakage or abuse is avoided.

The system receives a voice query at an audio interface and converts the voice query to text. The system can determine pronunciation information during conversion and generate metadata that indicates a pronunciation of one or more words of the query, include phonetic information in the text query, or both. A query includes one or more entities that may be more accurately identified based on pronunciation. The system searches for information, content, or both among one or more databases based on the generated text query, pronunciation information, user profile information, search histories or trends, and optionally other information. The system identifies one or more entities or content items that match the text query, and retrieves the identified information to provide to the user.

An embodiment user terminal includes an image acquisition part, a user interface configured to display an image photographed through the image acquisition part, a position detection sensor configured to detect a position of the user terminal, and a controller configured to determine a recommended point of interest (POI) among a plurality of POIs located around the user terminal, based on operation information about each of the plurality of POIs, a user's search history for a POI, and the user's POI scrap, and to control the user interface to display an augmented reality (AR) image corresponding to the recommended POI by superimposing the AR image on the image photographed through the image acquisition part.

The invention discloses an intelligent anti-cancer science popularization content recommendation method and system based on patient big data, and the method comprises the following steps: collecting patient data, including static data and behavior data, the behavior data including search history, click record, browsing duration, activity participation record, and reading content; extracting user features from the patient data, and generating a user portrait; anti-cancer popular science contents in the platform are collected, and labels are added to the popular science anti-cancer contents by using a natural language processing technology and a machine learning algorithm; constructing a content recommendation engine for dynamically matching the user portrait with the tag; according to the matching result of the content recommendation engine, generating a personalized content recommendation list in combination with the current anti-cancer treatment stage of the patient; and transmitting the recommended content by using multiple channels according to the content recommendation list. According to the method, the user portrait is constructed through the multi-dimensional data of the patient, so that the pertinence and adaptability of recommendation can be effectively improved.

The purpose of the present invention is to provide an interactive system that allows addition of appropriate response content. An interactive system 100 includes a history DB 105b that stores search history information containing an acquired key, which is a keyword acquired from an input sentence acquired by user operation, a new candidate key, which is an unknown word, and a query sentence, which is response content retrieved using the acquired key. A query sentence generation unit 109 generates a new query sentence on the basis of the search history information, by using a search key contained in the query sentence and the new candidate key.

The invention discloses an animation design material library management method based on big data, and relates to the technical field of animation design, and the method comprises the following steps: obtaining all materials in a material library and attribute information of the materials, the attribute information specifically comprising resolution, format, style and use scene; classifying all the materials in the material library according to the attribute information of the materials, and outputting a classification result of the materials in the material library; acquiring historical user behavior data of the material library, wherein the historical user behavior data specifically comprises search history, download records, use scenes and recompilation behaviors; according to the steps 1-3, the problems that an existing material library is not clear in classification and insufficient in personalized recommendation are solved, efficient retrieval and personalized recommendation of materials are achieved through refined classification and labeling processing in combination with precise calculation of the user preference degree, the screening cost of a designer is reduced in the process, and the screening efficiency of the designer is improved. And the convenience and the use efficiency of material searching are obviously improved.