Methods, systems and computer program products for recommendation systems. Embodiments commence by gathering a set of pathnames that refer to content objects of a collaboration system. A tokenizer converts at least some of the pathnames into vectors. The vectors comprise hierarchical path components such as folder names or file names, which vectors are labeled with an indication as to whether or not the folder or file referred to in a particular vector had been clicked on by one or more users. Some portion of the labeled vectors are used to train a predictive model. Another portion of the vectors are used to validate the predictive model. When the model exhibits sufficient precision and recall, the predictive model is then used to predict the probability that a particular user would have an interest in a particular folder or file. The folder name or file name is presented as a collaboration recommendation.

A method for enhanced digital content presentation is provided that includes assigning, to each member of members of a social network of a user, a respective knowledge score, the knowledge score being representative of accuracy in the member predicting positive impressions of the user. The members of the social network are surveyed for an indication of which one or more options for digital content are expected to have a positive impression on the user. Based on the surveying, selected digital content based on the one or more options that are expected to have a positive impression on the user, as indicated by the surveying, is selected. The selected digital content is presented to the user. An indication of whether the selected digital content had a positive impression on the user is received from the user.

The disclosed computer-implemented method may include (1) sampling links from an online system, (2) receiving, from a human labeler for each of the links, a label indicating whether the human labeler considers a landing page of the link to be a low-quality webpage, (3) deriving features from a landing page of each of the links, (4) using the label and the features of each of the links to train a model configured to predict a likelihood that a link is to a low-quality webpage, (5) identifying content items that are candidates for a content feed of a user of the online system, (6) applying the model to a link of each of the content items to determine a ranking of the content items, and (7) displaying the content items in the content feed of the user based on the ranking. Various other methods, systems, and computer-readable media are also disclosed.

Systems, apparatuses, and methods are described for adjusted exposure threshold values. Each user may be associated with one or more of a plurality of exposure threshold values, for example, a frequency cap value, that may be a cap (e.g., threshold) on the number of times a repeat advertisement content may be output to a user. Exposure threshold values may be modified based on one or more viewing statistics, for example, information about one or more interactions of the user associated with one or more outputs of repeat advertisement content, environment information, and/or one or more advertisement characteristics.

One example system event recommendation includes a processor and at least one memory device. The memory device includes instructions that are executable by the processor to cause the processor to receive a plurality of user features associated with a user, receive a plurality of event features, each of said event features associated with one or more events, and determine a predicted favorite score personalized for each user for each of the one or more events, the predicted favorite score based at least in part on the plurality of user features and the plurality of event features. The memory device also includes instructions that cause the processor to display at least one of the one or more events to the user based at least in part on the personalized predicted favorite score associated with each of the one or more events.

An example apparatus includes processor circuitry to: access first input data from meters, the meters to monitor media devices associated with a plurality of panelists, the first input data including media source data and panel data; reduce a dimensionality of the first input data to generate second input data of reduced dimensionality relative to the first input data, the dimensionality of the first input data to be reduced based on a prior probability of an audience rating associated with the plurality of panelists and an approximation of a dependency of the audience rating on at least one of the media source data and the panel data; and decode the second input data of reduced dimensionality to output a probability model parameter for a multivariate probability model, the multivariate probability model having dimensions corresponding to the first input data, the multivariate probability model to label census data.

Systems and methods for determining whether and how to present digital animations in a user interface are disclosed. According to certain embodiments, the systems and methods may facilitate the identification of a set of products or services purchased by an individual, and the determination of a reward level associated with the set of products or services. The systems and methods may select a digital animation, from a set of digital animations that is predetermined based on a set of probabilities, corresponding to the reward level, and present the digital animation in a user interface.

Embodiments of the present disclosure provide a livestreaming data processing method and apparatus, and a device. The method comprises: determining historical contribution values of a plurality of users viewing first livestreaming; if the historical contribution values of at least two of the plurality of users are greater than or equal to a first threshold, displaying a preset object in a livestreaming page of the first livestreaming; obtaining real-time contribution values of the plurality of users to the first livestreaming; and if it is determined, according to the real-time contribution values of the plurality of users to the first livestreaming, that there is a target user in the plurality of users, determining an owner of the preset object as the target user, the real-time contribution value of the target user to the first livestreaming being greater than or equal to an object contribution value corresponding to the preset object.

Methods, systems, and computer products for placement of directed content, such as advertisements, are provided. In some examples, advertisement recommendations are received from one or more third party advertising recommendation platforms. The advertisement recommendations are for items offered for sale via the retail website. Requests and/or advertisements may be filtered, based on customer context, item availability, and the like, by a retail enterprise. The plurality of available advertisements may be mediated using a weighted reverse index model based on at least first data tracking a plurality of relevancy ranks and second data tracking a plurality of bid values, wherein each of the plurality of available advertisements corresponds to one of the plurality of relevancy ranks and one of the plurality of bid values. A predetermined number of advertisements may be provided to a customer system for display within a webpage of a retail website.

A method includes identifying, by a server, a content location assigned to a first content slot of a schedule template defining a display rotation loop based on multiple content slots. Each of the content slots in the schedule is associated with slot criteria, and the schedule template is associated with a digital display. The server generates schedule data that indicates the display rotation loop, the content location, and the slot criteria. Dynamic content retrievable from the content location is displayable at the digital display at a first time. The first time is based on the first slot criteria of the first content slot. The server transmits the schedule data via a network to the digital display.