A system and methods for evaluating whether a public review matches another review is disclosed. The method includes obtaining a private review and a public review of a business. A likelihood the public review matches the private review is evaluated. The evaluation includes evaluating a match distance between text bodies, reviewer names, review dates and ratings in the public review and the private review. Evaluation rules are applied to determine the likelihood of a match.

Systems and methods may be used to predict an applicable a hearing assistance device shell or model. For example, a method may include obtaining patient information, determining, using a machine learning trained model, a correlation between an input vector and each of a plurality of feature vectors corresponding to a plurality of hearing assistance device models, and ranking the plurality of hearing assistance device models based on respective correlations to the input vector. Information corresponding to a highest ranked hearing assistance device model may be output.

Systems and methods for generating a content item based on a difference between a user confidence score and a confidence score are disclosed. For example, a system generates for output a first content item. While the first content item is being outputted, the system receives user data via sensors of a device. The system determines a user confidence score based on the user data and metadata of the first content item. The user confidence score indicates a user's perceived probability of an event occurring in the future. The system calculates a prediction score which estimates the likelihood of the event occurring in the future. In response to determining that the difference between the user confidence score and the prediction score exceeds a threshold, the system selects a second content item related to the event and generates for output a recommendation comprising an identifier of the second content item.

In some embodiments, a method receives a sequence of subsidiary features that are associated with a sequence of main features. A subsidiary feature provides subsidiary information for a main feature. A sequence of first weights for the sequence of subsidiary features is generates where a first weight in the sequence of first weights is generated based on a respective subsidiary feature. The method processes the sequence of first weights to generate a sequence of second weights. The processing uses relationships in the sequence of first weights to generate values of the second weights. The method uses the sequence of second weights to process the sequence of main features to generate an output for the sequence of main features.

A reduced bias submission review system distributed on one or more servers operably coupled to one or more client computing devices over a network supports a program including computer executable instructions accessible by browser based on-line processing to enable one or more clients to submit a plurality of submissions for submission review and depending on the application repeatedly serves the plurality of submissions in a prioritized order to a plurality of reviewers until each of the plurality of submissions receives a pre-selected target number of submission reviews or until each of the plurality of reviewers performs the preselected target number of submission reviews.

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.

A trade exchange system and method provide transparency and accountability for each idea producer and each follower of each idea producer with respect to trades executed based on a trade idea circulated by the idea producer. The trade exchange system and method may use application-programming interfaces (APIs) to gather data from a brokerage entity in order to verify the trades executed in response to the trade idea by the idea producer and the follower.

The invention relates to a method, a system, a computer program product and a service for providing up-to-date and verified information of an intermediate product-specific sustainability indicator. The method comprises receiving a first input data (102, 202, 302) including internal information on the intermediate product, receiving a second input data (104, 204, 304) including external information on raw material(s) used for the intermediate product, processing at least the first input data (102, 202, 302) according to a life cycle assessment method in order to produce an intermediate product-specific life cycle parameter, processing at least the second input data (104, 204, 304) in order to produce an intermediate product-specific sustainability parameter, providing an intermediate product-specific sustainability indicator (108) comprising the intermediate product-specific life cycle parameter and the intermediate product-specific sustainability parameter, and associating the intermediate product-specific sustainability indicator (108) for the intermediate product.

The present disclosure relates to a computer-implemented process for generating and providing simulated user absorption information pertaining to users and based on target profiles and target situations, thereby providing user targeted and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user profile targeted content recommendations for users of an interactive electronic system.

The present invention relates to a rating and scoring system and application for landlords and tenants. The system allows both landlords and tenants to obtain a preview of how each other operates prior to signing a lease, ensuring both parties can maintain a positive working relationship. The system is configured to use a plurality of ranking parameters for the landlords and tenants wherein the tenants and other landlords registered in the system provide ranking for a landlord and similarly, the landlords and other tenants may provide ranking for a tenant. An average of all the rankings for a tenant and landlord is used for an overall score. The overall score along with individual parameter ranking provide a quantitative representation of quality of the tenant and landlord and enables individuals to find compatible landlords and tenants.