A method of selecting and presenting content based on learned user preferences is provided. The method includes providing a content System including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

A system for ranking geospatial entities is described. In one embodiment, the system comprises an interface for receiving ranking data about a plurality of geospatial entities and an entity ranking module. The module uses a ranking mechanism to generate place ranks for the geospatial entities based on the ranking data. Ranked entity data generated by the entity ranking module is stored in a database. The entity ranking module may be configured to evaluate a plurality of diverse attributes to determine a total score for a geospatial entity. The entity ranking module may be configured to organize ranked entity data into placemark layers.

Aspects of the present invention customize services, including search results, that are customized based on the user's familiarity with a geographic area. The search results may be customized by including different information in a search result, emphasizing different information in a search result, adjusting a relevance rank of different search results, and such. The search results may be customized based on the understanding that a user's familiarity with a particular area may influence the type of information that can be useful to the user. Different levels of familiarity may cause different types and levels of search result customization to be performed.

In some embodiments, apparatuses and methods are provided to enable wide access to numerous different previously compiled forecast modeling. In some embodiments, a system is provided that enables wide access to forecasting, comprising: a forecast model database that maintains numerous different forecast models that when run produce resulting forecast data relevant to making business decisions; and a forecasting interface system configured to receive multiple different forecast requests for forecast request data, which comprises a forecast model index comprising identifiers of the numerous different predefined forecast models and for each of the numerous different forecast models relevance characteristics, wherein the forecasting interface system selects, for each received forecast request, a forecast model of the numerous different forecast models based on a relationship between the corresponding forecast request data and the relevance characteristics.

Techniques are disclosed utilizing cognitive computing to improve banking experiences. A customer's account may be monitored to determine an amount of customer interactions, and that the amount of customer interactions for the customer through at least one self-service channel is less than the amount of customer interactions for the customer through at least one full-service channel. When the amount of customer interactions for the customer through the at least one self-service channel is less than the amount of customer interactions through the at least one full-service channel, the system may generate an electronic offer for the customer that includes a reward for an account associated with the customer if the customer increases usage of the at least one self-service channel and decreases usage of the at least one full-service channel for future customer interactions with the vendor.

Offline consumer behavior and interactions are observed using beacons positioned at venues where consumers spend time and interact with each other. The beacons communicate with mobile computing devices that are carried by the observed consumers. The observed consumer behavior provides actionable insights into how consumers influence each other. For example, the people that a particular consumer spends time with form a “circle of influence” associated with that consumer. If the consumer makes a purchase, members of the circle of influence are observed to understand the degree to which they were influenced by the purchase, if at all. Metrics that quantify a consumer's influence over other consumers allow marketers to more effectively target both the influencing and influenced consumers. Also, if relatively little information is known about a particular consumer, that consumer's digital marketing profile can be supplemented using information known about the consumers with whom he/she often spends time.

A method and a system receive and process multiple streams of input and output a stream of output that generates a user interface based on the input. The user interface includes one or more questions generated in response to the streams of input and is transmitted electronically to a user. The user provides further input to the method and the system through the generated user interface.

This present disclosure is related to systems and methods of collecting and analyzing data from cannabis users (e.g., cannabis influencers and trend setters), social media websites that may geared towards deciphering cannabis trends and preferences. Such a system may allow the cannabis producers (growers, extractors, cannabis product manufacturers) to collect data about the effects and/or qualities of their product and may also allow users to be fully informed prior to purchasing. This system may generate reports on a monthly or weekly basis for the convenience of its users. It is a method of collecting feedback from users of cannabis concentrates via a social network that is geared to power users, influencers, and trend setters. The system is marketed to stakeholders that are making products for consumers, craft makers, and high-end users as a platform to exchange information about what concentrate profiles are best for different end products, along with processing steps. This is a subscription program for cannabis concentrate users that allows cannabis companies to collect data about the effects and/or qualities of their products.

Quantitative evaluations of friction within a customer experience may be provided to reduce abandonment and improve conversion of transactions. One or more transactions may be identified. One or more personas corresponding to the one or more transactions may be identified. One or more customer friction factor (CFF) scores corresponding to the one or more transactions may be determined. A given CFF score may be a quantification of an aspect of a given transaction that has a negative impact on a customer experience associated with the given transaction. The one or more CFF scores may be compared with one or more valid comparisons relating to the same industry and the same transaction type. One or more friction points may be identified based on the comparison of the one or more CFF scores. Addressing a friction point may reduce abandonment and improves conversion associated with transactions.

The learning unit 81 learns an attribute viewpoint model in which attributes of a target are explanatory variables for each target person so as to minimize a difference between a prediction result by a predictor that predicts an evaluation result of each target person based on a feature vector of the target person and a prediction result by a prediction model that predicts an evaluation result learned for each target person, using the attributes of the target as explanatory variables. The attribute generation unit 82 generates an attribute so that an evaluation result obtained according to the attribute applied to the learned prediction model satisfies the specified objective.