A publishing user identifies a product offered by the user to an online system by providing multiple images of various products viewed at different angles to the online system. The online system applies an identification model to content items obtained from other users to identify one or more of the products in various content items. From a number of content items obtained from other users that include products offered by the publishing user, the online system compute trends. The trends may be for a specific product or products having one or more common attributes. The online system transmits information about the trends to the publishing user or to other users. The trends may also be used to rank recommendations for products to a specific user, where the trends from numbers of content items including products are weighted by the specific user's preferences.

An approach is provided for determining the context-aware similarity between applications. A similarity platform processes one or more context logs associated with one or more devices to determine one or more context intents. The similarity platform further determines one or more distributions of one or more applications executed at the one or more devices with respect to the one or more context intents. The similarity platform also determines a context-aware similarity between two or more applications based, at least in part, on the one or more application distributions.

Provided is a process of distributing offers to non-location-sensing devices based on a geolocation sensed by another device, the process comprising: obtaining a geolocation of a user from a mobile device of the user; associating the geolocation of the user with an identifier of the user in an offers engine; receiving a request for an offer from another computing device of the user, the request including or prompting obtaining data from the other computing device sufficient to identify the user; retrieving the geolocation of the user obtained from the mobile device by identifying the user with the data from the other computing device sufficient to identify the user account; selecting, with the offers engine, a responsive offer based on the geolocation of the user obtained from the mobile device; and sending the responsive offer to the other computing device of the user.

A service provider may receive item price information and/or point of sale (POS) transaction information from a plurality of merchant devices associated with a plurality of merchants. The service provider may categorize the plurality of merchants according to the types of items offered by the respective merchants, and may further categorize the merchants according to the locations at which they conduct business. The service provider may subsequently determine a price recommendation or other pricing information for a particular item offered by a particular merchant. A merchant device of the particular merchant may present the pricing information in a user interface that enables the merchant to view projected sales at various different price points. Further, the merchant may be able to specify one or more factors used for determining the recommend price, such as a desired business model, a desired clientele, a desired profit margin, or the like.

Aspects of the subject disclosure may include, for example, a method including receiving advertisements from a remote advertising content provider and pre-loading the advertisements at a communication device. An advertisement is selected for presentation at a presentation device, based on a user's advertising viewing history, the duration of the advertisement, the device location, the device environment, the user's demographic profile, or the user's activities. The presentation device presents the advertisement responsive to the trigger message. The effectiveness of the advertisement is determined, and the advertisement viewing history is updated; the advertisement is selected based in part on the effectiveness of a past presentation of the advertisement. Other embodiments are disclosed.

Embodiments of the present specification provide deep reinforcement learning methods and apparatuses for referral marketing. One method includes the following: obtaining state information of an execution environment of a deep reinforcement learning system, wherein the state information comprises user information of a current user of the deep reinforcement learning system; determining a marketing activity corresponding to the state information based on a marketing strategy, wherein the marketing activity comprises a combination of a marketing channel, marketing content, and a marketing time period; obtaining a reward score of the execution environment for the marketing activity; and updating the marketing strategy based on the reward score.

In general, embodiments of the present invention provide systems, methods and computer readable media for a predictive recommendation system using predictive models derived from tiered feature data.

Systems and methods for redeeming a reward held by an individual are described. A method for redeeming a reward includes determining threshold criteria for provision of a targeted reward redemption offer, identifying at least one individual based upon the threshold criteria, determining the targeted reward redemption offer, and providing the targeted reward redemption offer to the identified individual. A response to the provided targeted reward redemption offer may be received, and an account of the identified individual may be adjusted in accordance with the targeted reward redemption offer and the received response.

Aspects of the subject disclosure may include, for example, a method in which a processing system obtains physical and social environmental data for a communication device user, and provides content for presentation at the device. First reaction data, obtained via sensors associated with the user, indicate the user's reaction to presentation of the content; the data is analyzed to determine user affinity for the content in a context of the physical and social environments. The content is modified during the presentation; second reaction data is obtained and analyzed to determine a second user affinity for the modified content. If the affinity is enhanced, the modified content is sent to other users' equipment via a social network. Affinity responses regarding the modified content are analyzed, and a set of users is identified as an affinity group; additional content is transmitted to equipment of the affinity group. Other embodiments are disclosed.

In a system for dynamically selecting messages for display on a mobile device, the message to be displayed is selected based on a change in one or more parameters associated with the user of the device. The parameters include a set of promotions previously transmitted to the user device or a purchasing pattern of one or more other users to whom a particular promotion was sent previously.