The present application discloses a method, an apparatus, a device, and a storage medium for intention recommendation, which relates to the field of big data, artificial intelligence, intelligent search, information flow and deep learning technologies in the field of computer technologies. A specific implementation scheme includes: receiving an intention query request carrying an intention keyword and a user identification, determining a first recommendation list according to the intention keyword and a pre-configured intention repository, where the intention repository includes at least one tree-shaped intention set, and each tree-shaped intention set includes at least one graded intention, processing intentions in the first recommendation list by using intention strategy information corresponding to the user identification to obtain a target recommendation list and output it.

Methods and systems for facilitating generation of electronic advertising messages based on model web pages. A visual content request is received that identifies a model web page. Source code is retrieved that includes patterns that can be matched to identify visual features of the model web page. Visual content information is provided to an advertising message generation service to construct advertising messages that include at least a portion of the identified visual features. Constructed advertising messages are received, including at least one modified advertising message that incorporates a user-selected image extracted from the model web page. The identified visual features of the model web page can include text format, text color, background color, images, and video sequences.

Disclosed are various embodiments for establishing a connection between a client device and a third-party entity device and providing services associated with a third-party entity to the client device according to user-defined access permissions. A context environment can be determined according to user data and third-party entity data. Services available to the user device can be selected according to the context environment, the user-defined access permissions and third-party defined instructions. Upon selecting the services, the services are provided to the client device and a connection between the client device and a third-party entity device can be established.

In one aspect, an example method to be performed by a vehicle-based media system includes (a) receiving audio content; (b) causing one or more speakers to output the received audio content; (c) using a microphone of the vehicle-based media system to capture the output audio content; (d) identifying reference audio content that has at least a threshold extent of similarity with the captured audio content; (e) identifying visual content based at least on the identified reference audio content; and (f) outputting, via a user interface of the vehicle-based media system, the identified visual content.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Embodiments of the present invention provide systems and methods of log optimization for television advertisements. Exemplary method and systems can comprise: receiving, at a computer software platform, (i) the digital log, (ii) constraint parameters, and (iii) viewership predictions; and generating, with the computer software platform, an optimized log based on the received digital log, the constraint parameters, and the viewership predictions.

A press release distribution system provides press release and other news to forum sites as posts. The forum software that runs at forum sites includes press release interface software or is adapted to receive press release interface plug-in modules for interfacing with the press release distribution system. The press release interface software or plug-in module may also monitor and/or analyze user data of forum members and/or forum activities of the users. The monitored user data and forum activities may be provided to the press release distribution system for analysis and generation of user profiles. Using the result of the analysis (e.g., user profiles), the press release distribution system can target particular users or forums to direct the press releases, news, or advertisements for most effective advertising campaign.

An equipment rental system comprising a kiosk, the kiosk comprising a user interface configured to receive user input and provide outputs to a user, a processor configured to receive the user input from the user interface, generate user output and control a locker system; and a locker system configured to store the equipment and allow access to the equipment under control of the processor, the processor configured to: receive log in and credential information form a user via the user interface, present the user a choice of equipment, receive a selection of equipment through the user interface, gather payment information from the user through the user interface, wherein the payment information corresponds to a period of use for the selected equipment, and control the locker system to make the selected equipment available to the user.

A computer-implemented, network-connected content recommender generating content recommendations for a plurality of content servers hosted by one or more customers, the content recommender comprising: one or more processors; a memory storing instruction that, when executed by the one or more processors, cause the recommender to perform operations comprising: receiving a plurality of content recommendation requests from a querying one of said customer content servers via a plurality of input streams, each input stream including a data repository; outputting data, from the memory, associated with the content recommendation requests; receiving some or all of the data associated with said content recommendation requests; generating a first model-specific recommendation result from a first set of the plurality of received data; generating a second model-specific recommendation result from a second set of the plurality of received data; combining the first model-specific recommendation results with the second model-specific results to generate an ensemble recommendation result; and transmitting the ensemble result from the content recommender to said querying customer content server.