614 results about "Recommendation service" patented technology

A system provides a user interface through which users can flexibly tag individual items represented in an electronic catalog with user-defined tags, such as text strings, and obtain recommendations that are specific to particular tags. The tags and tag-item assignments created by each user are stored persistently in association with the user, and may be kept private to the user or exposed to others. Once a user has assigned a tag to a number of items, the user (or another user in some embodiments) can request and obtain recommendations that are specific to this tag. These recommendations may be generated in real time by a recommendation service that identifies items that are collectively similar or related to the items associated with the tag.

A context-aware service recommender system receives current user context and recommends a list of browser-based services to a user on a mobile devices. A user's mobile device receives context events from smart environments in which the mobile device is operating. Data about the context events is relayed to a service recommendation server. The server develops recommendations based on the context and other factors, and relays information about the recommended services to the mobile device. As each recommended service is selected or ignored by the user of the mobile device, the device sends implicit feedback with this information to the service recommendation server for use in subsequent recommendations.

A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins. The bin packing problem develops the recommended server configuration based on reducing a total acquisition cost while satisfying the constraints and the sizes of the applications.

A media item recommendation rating system and method. A recommendation rating for media items is established and dynamically updated in response to media items being recommended to other users. A recommendation server or other device receives a report of a media item recommendation and updates a recommendation rating in response. The recommendation rating may also be updated based on how often a recommended media item is used or played. Thus, a media item's recommendation rating is affected by events relating to its recommendation, as opposed simple play-based ratings that are updated on any play action regardless of whether related to a recommendation or not. Simple play-based ratings do not distinguish between ordinary usages or plays and those resulting from recommendations. Recommendation of a media item to another user may be a better indicator of the user's likeability or popularity of a given media item, since a recommendation is an endorsement by another.

One embodiment of the present invention provides a system for recommending an application-feature to a user. During operation, the system receives application-usage information from a client at a recommendation-server, wherein the application-usage information specifies characteristics of a user's interaction with an application. Next, the system compares the application-usage information to additional application-usage information from other users to identify a usage-group, which contains users who use the application similarly to the user. The system then identifies an application-feature associated with the usage-group, but which is not associated with the user. Finally, the system sends to the client an application-feature identifier, which identifies the application-feature, to facilitate recommending the application-feature to the user.

The invention provides an academic resource recommendation service system and method. The method comprises the following steps: crawling academic resources on an internet by using an LDA (Latent Dirichlet Allocation)-based focused crawler, classifying the academic resources according to preset A types by using an LDA-based text classification model, and storing the academic resources in a local academic resource database, wherein the system further comprises an academic resource model, a resource quality value calculation module and a user interest module; implanting a tracking software module at a user terminal, combining interesting subjects and historical browsing behavior data of the user, respectively modeling the academic resource model and the user interest module by virtue of four dimensions such as the academic resource type, subject theme distribution, key word distribution and LDA latent theme distribution, calculating the similarity between the academic resource model and the user interest preference module, combining the resource quality value to calculate the recommendation degree, and finally perform academic resource Top-N recommendation for the user according to the recommendation degree. According to the method disclosed by the invention, personalized accurate recommendation of the academic resources is performed according to the identity, interest and browsing behaviors of users, and the working efficiency of scientific research personnel is improved.

The invention discloses an individualized service recommendation system and a corresponding method including the following steps: various operation information existing on a terminal is monitored by a user information collector and after a pre-processing the operation information is stored in a user information database; and if the user information database is updated, a user behavior analyzer is started for analysis; the user behavior analyzer scans the user information database and extracts the new user information and stores the new information in a resource information database, and then new recommendation strategies are computed and stored in a recommendation strategy database; a context sensing processor senses the present context of the user and sends out the present context description information and an individualized recommendation processor is started; the individualized recommendation processor acquires the present context information after receiving the information from the context sensing processor and also acquires a matched optimal recommendation strategy by searching the recommendation strategy database and then matches the proper resource information according to the optimal recommendation strategy and by searching the resource information database so as to generate the individualized recommendation service in real time.

A media recommendation system for recommending media content that is historically related to seed media content is provided. The recommended media content may be songs, television programs, movies, or a combination thereof, and the seed media content may be a song, television program, or movie.

The invention discloses a field-oriented personalized intelligent recommendation system and an implementation method. Aiming at the defect of poor universality of the traditional recommendation manner, a reasonable data organization mode is adopted, so that the recommendation service has a cross-field property, and field conversion can be realized by configuring field information according to the characteristics of different fields; aiming at the problem of data sparseness caused by single user information acquisition source and low quantity in the traditional recommendation manner, a context perception technology for actively acquiring context information of interactive behaviors of a user and the system is introduced, so that the quantity of the acquired user information is greatly increased, and the time distribution of the acquired user information is more reasonable; the acquisition of the user information is directly related with the interactive behaviors of the user and the system; the system can acquire the current condition of the user in real time, so that the recommendation service can dynamically reflect the change condition of user demand; and the recommendation service quality is improved through a recommendation learning model by continuously using the reflection of the user on the recommendation result.

A recommendation service is provided for recommending computer video game titles to players. The recommendation service offers suggestions for game titles to purchase or rent based on playing usage related parameters for each particular player. A profile is created based on several factors that represents the player's affinity to each factor. Communication and use of player usage data may be strictly conditioned on a player's knowledge and consent.

A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.

The invention discloses a method and a system for recommending multimedia applications. The method comprises the steps of: calculating recommendation parameters corresponding to users according to historic behavior characteristics of the users using the multimedia applications, wherein the recommendation parameters represent the preference degree of the certain user for the at least one multimedia application; calculating the similarity between the at least two multimedia applications according to the current recommendation parameters; generating initial multimedia application recommendation lists for the users according to the similarity; and obtaining the initial multimedia application recommendation lists when receiving the recommendation requests of the current users so as to show the initial multimedia application recommendation lists at the clients of the current users. The method and system disclosed by the embodiment of the invention have the advantages of being capable of supplying different and personalized multimedia application recommendation services for the different users and increasing the accuracy and success rate of multimedia application recommendation.

The invention discloses a book recommendation method based on user actions. The book recommendation method comprises the following steps of: calculating user-to-book interestingness of each user to browsed books according to book browsing time, access times, the number of access paths, access times of each access path, the depth of each access path and the number of content bytes of the books of each user in a present day; and calculating the similarity among the users on the basis of the user-to-book interestingness, selecting a plurality of neighbor users with high similarity for a target user, and recommending the books which are read by the neighbor users but not read by the target user to the target user. The book recommendation method belongs to the technical field of e-commerce information retrieval and treatment based on a mobile internet, and can be used for exploring the preferences of the users according to the book browsing actions of the users so as to provide relatively accurate book recommendation service to the users.

An application store tastemaker recommendation service is usable to determine experts within a user's social network(s), receive recommendations from the experts, and filter and / or rank mobile application query results based at least in part on the recommendations. Additionally, the service may be usable to determine experts based on data compiled about previous actions, reviews, comments, etc., of the experts. Further, the service may be usable to provide recommendations to the user to aid in selecting mobile applications for purchase, and may provide an avenue for completing such purchases.

A content recommending server includes: a content information collecting section collecting content information including metadata of contents from a content server through a network; a content database storing the content information collected by the content information collecting section; a user profile collecting section collecting user profiles of users from user terminals through the network, each of the user profiles including each user's preference; a user profile database storing the user profiles, the user profiles including a subject user profile; a content indexer acquiring the metadata and generating content indices of the contents; a user indexer acquiring the user profiles from the user profile database and generating user indices of each of the users; an index database storing the content indices and the user indices; and a content recommending section receiving the subject user profile, searching the index database for an certain index corresponding to the subject user profile, and determining a recommend content.

A method and system for content recommendation make such recommendations possible, even if users are in different geographic locations, use different network service providers and different services, user different types of device, etc. A method for communicating a content recommendation comprises receiving a recommendation for content, the content recommendation originating from a first client device communicating using a first network and destined for a second client device communicating using a second network, determining access information for the second client device to access the content using the second network, and transmitting the determined access information.

The invention discloses a personalized portal recommendation services method and system using Yuan recommendation engine, and provides portal users interested models construction, including the initial creation and the follow update of portal users' interested model. It provides Personalized Recommendation Service Architecture independent to the portal platform by using Yuan recommendation engine, and Yuan recommendation engine can analyze the association of users and user groups, and personal interest changes, and unified control information resources and recommended algorithm to reasonable choice, and optimized push and produce more comprehensive variety personalized recommendations. In resources display, the system with personalized portal recommendation services can package a variety of Web content objects predicted to be recommended into portal component, give the portal users vivid and direct personal display, and provide a higher level of personalized control. Comprehensive utilization of the existing personalized resources and technical means of the portal platform provides independent and flexible services middleware or services agency, to complete the personalized recommendation services.

The invention relates to a commodity recommending method and commodity recommending system. The method comprises obtaining sample training data based on browsing history of users and / or user attributes, and establishing an associated analysis model for the sample training data; generating associated data based on the associated analysis model; accepting commodity recommending requests, and obtaining user information based on the commodity recommending requests, recommending commodities associated with the user information to relevant portals based on the user information and the associated data, so that relevant portals can show commodities to users. The commodity recommending system comprises a modeling unit and a commodity recommending server. The technical scheme not only guarantees recommending qualities, but also provides high-speed real-time recommending service, and has the advantages of being high in expandability, and suitable for different application contexts.

A recommendation system comprises a recommendation server (107) which generates a first recommendation set of recommended content items in response to a user profile associated with a first user and stored on the recommendation server (107). Content item identification data identifying the content items of the first recommendation set are transmitted to a first recommendation device (101). The first recommendation device (101) comprises a network interface (301) which receives the content item identification data from the recommendation server (107). A content list processor (303) determines the first recommendation set in response to the content item identification data. The first recommendation device (101) furthermore comprises application processors (309-313) which can execute different recommendation applications. A device recommender (307) generates a second set of recommended content items from the first recommendation set in response to a characteristic of the recommendation application being executed. The application then provides recommendations in response to the second set.

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.

Content recommendation apparatus and methods using a tag cloud provide a content recommendation service via a network. The apparatus includes a content tag cloud generating module configured to generate a content tag cloud by analyzing a tag assigned to each content and accumulating frequencies per tag of each content. The apparatus also includes a user tag cloud generating module configured to generate a user tag cloud by accumulating frequencies per tag of contents used by a user. The apparatus further includes a similarity computing module and a recommending module. The similarity computing module is configured to compute a similarity between users using the user tag cloud, and the recommending module is configured to recommend content by computing a probability that a target user will use a specific content based on the computed similarity between users.

Embodiments of the invention provide a music recommendation method and apparatus. The music recommendation method comprises the steps of detecting an event of initiating a search request by a user, and obtaining environment information according to a scene of initiating the request by the user; extracting request parameters of the user according to the search request, wherein the request parameters include a user model, the environment information and a tag of music content in the search request; screening out candidate music from a database according to the tag of the music content; and computing matching scores of the user and the candidate music in combination with the music model according to the user model, sorting the candidate music according to the matching scores, and recommending the sorted candidate music to the user. Correspondingly, the embodiment of the invention furthermore provides the music recommendation apparatus. The music recommendation apparatus comprises a detection module, an extraction module, a screening module and a recommendation module. According to the technical scheme provided by the embodiments of the invention, accurate recommendation services matched with user preferences are provided for the user, so that the personalized demands of the user can be met and the user experience is improved.

The invention discloses a dynamic recommendation method based on training set optimization for a recommendation system, which specifically includes: (1) establishing a preliminary recommendation portion: generating an original recommendation model according to original user grading data; (2) performing AdaBoost trainings: utilizing the original recommendation model as a classifying and judging basis to classify the data and adjust learning times of samples by means of multiple iterative learning training data; (3) screening incorrect samples: data of selected difficult samples are removed as the incorrect samples after multiple AdaBoost trainings so as to construct a new training data collection; (4) reconstructing a recommendation model: combining training results to regenerate the recommendation model based on the new training data; and (5) generating recommendation results: utilizing the new recommendation model to generate the recommendation results. The method is capable of removing the data without referential meaning in recommended service by the aid of great relevance of original training set data in content, so that validity of the training data and precision of the final recommendation model are improved.

The present invention discloses a sequence recommendation method based on user behavioral difference modeling. The method comprises: acquiring historical behavior information of a user; calculating acommodity feature vector according to the acquired historical behavior information; by combining a commodity feature vector, using a behavioral difference modeling method for sequence modeling, and obtaining the current demand and historical preferences of the user by using two different neural network architectures; and according to the current purchase demand and the historical preferences of the user, predicting the next commodity of interest to the user through joint learning, performing matching in a commodity vector space, finding a plurality of commodities that are closest to the predicted result in the commodity vector space, and generating a commodity recommendation sequence. According to the method provided by the present invention, through difference modeling on user timing sequence behaviors, the current demand and long-term preferences in the purchase decision of the user can be intelligently understood, and accurate sequence recommendation services can be provided for users.

Systems and methods have been developed for selecting and recommending events to a consumer of media content based on information known about the consumer, including the consumer's interests, consumption history and preferences. In an embodiment, a system records a history of the media content accessed, purchased, viewed or otherwise consumed by a consumer. The system further includes an event database containing information about upcoming events. When a consumer interacts with the system, such as to access a media content item, the consumer is notified of one or more events based on the consumer's history. In this way consumers are automatically provided with updated event recommendations based on the known interests of the consumer, without the need for the consumer to subscribe to an event recommendation service and maintain the subscription to accurately reflect the consumer's current interests.

The present invention relates to a personalized travel route recommendation method based on tourist trust. Firstly, photo information with a geographical label and historical tourist information in a network are gathered, a large number of photo information is subjected to preprocessing, reliable interest point information is obtained, then a body database is constructed by using body-based modeling thought, interest point probability is predicted by using the mixed model of Markov and an subject, the travel route generation algorithm based on interest point heat is generated, and finally combined with a user trust weighted tourist route, a final route is recommended to a user. According to the method, the real travel information of users in a social network is fully utilized, a personalized travel route recommendation service can be effectively provided to the user, and the method has a good reference value for transportation service departments and travel agencies.