Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: One embodiment of the present invention sets forth techniques for generating recommendation sets for a first client device. A recommendation system receives, from the first client device, a first selection of a first recommended item included in a plurality of recommended items. The recommendation system identifies a second recommended item included in the plurality of recommended items that has not been selected. The recommendation system retrieves an authenticated negative item from a plurality of authenticated negative items. The recommendation system stores one or more entries in a log file comprising a plurality of entries, based on at least one of the first recommended item, the second recommended item, and the authenticated negative item. One advantage of the disclosed techniques is that the use of authenticated negative examples, also referred to herein as authenticated negative items, provides a more relevant set of recommendations for the user.

Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: A data processing method comprises receiving title interaction data, wherein the title interaction data specifies, an order in which users interacted with a plurality of titles; generating a plurality of statistical models, each statistical model of the plurality of statistical models specifying a plurality of probabilities, wherein the plurality of probabilities represent, for each first title of the plurality of titles and each second title of the plurality of titles, a likelihood that a user will interact with the first title then next interact with the second title; refining the plurality of statistical models based on the title interaction data; determining a plurality of weight values corresponding to the plurality of statistical models for a particular user; identifying, for the particular user, one or more recommended titles of the plurality of titles based on the plurality of weight values and the plurality of statistical models.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: In one embodiment, a user login device may create a user identifier template 400 for identifying a user by implicitly capturing one or more biometric identifier readings. A user login device may capture an enrollment biometric identifier reading of a user during an operational user action. The user login device may apply the enrollment biometric identifier reading in creating a user identifier template 400.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: In an approach, a method comprises receiving title interaction data, wherein the title interaction data specifies, an order in which users interacted with a plurality of titles; generating a plurality of statistical models, each statistical model of the plurality of statistical models specifying a plurality of probabilities, wherein the plurality of probabilities represent, for each first title of the plurality of titles and each second title of the plurality of titles, a likelihood that a user will interact with the first title then next interact with the second title; refining the plurality of statistical models based on the title interaction data; determining a plurality of weight values corresponding to the plurality of statistical models for a particular user; identifying, for the particular user, one or more recommended titles of the plurality of titles based on the plurality of weight values and the plurality of statistical models.

Abstract: In one embodiment, a user login device may create a user identifier template 400 for identifying a user by implicitly capturing one or more biometric identifier readings. A user login device may capture an enrollment biometric identifier reading of a user during an operational user action. The user login device may apply the enrollment biometric identifier reading in creating a user identifier template 400.

Abstract: A system and method for dynamic pricing in a guaranteed display market includes: receiving attribute parameters and values for an incoming pricing query for an advertisement; calculating a base price for the advertisement using recent historical information from contracts matching the attribute parameters; calculating a price response by adjusting the base price to reflect market conditions; calculating a non-guaranteed display opportunity cost for the adjusted base price; and calculating a final price as a function of the adjusted base price and the non-guaranteed display opportunity cost, with the non-guaranteed display opportunity cost as a lower bound for the price.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: The present application provides systems and corresponding methods for creating a delivery allocation plan in a network-based environment. The methods may include receiving and storing advertising contracts and data related to the advertising contracts; constructing a bipartite graph based on the received contract data; annotating each demand node; and receiving impression data and other eligible contract data. Thereafter, the method may include for each impression, calculating a first supply value and for each contract, calculating a first demand value. The first demand value may be used to calculate a second supply value and a delivery allocation may be calculated using the second supply value and the second demand value for each contract.

Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: A method for pricing a contract for serving advertisements in an online display advertising environment comprising receiving a subject contract, the subject contract having at a target predicate for matching to a user visit, then forecasting, using a computer-based forecasting module, a set of user visits eligible to be served to the subject contract, wherein eligibility is based on matching the target predicate to a user visit (which user visit may be associated with an event predicate). Having a set of forecasted (matching) user visits, the method proceeds to select a set of eligible historical contracts that would be eligible to be served to the forecasted user visits. Finally, having a set of eligible historical contracts that would be eligible to be served to the target predicate, a curve fitting technique yields a price for the subject contract that minimizes the error in the price relative to expected user visits.

Abstract: An advertisement impression distribution system includes a data processing system operable to generate an allocation plan for serving advertisement impressions. The allocation plan allocates a first portion of advertisement impressions to satisfy guaranteed demand and a second portion of advertisement impressions to satisfy non-guaranteed demand. The data processing system includes an optimizer, the optimizer to establish a relationship between the first portion of advertisement impressions and the second portion of advertisement impressions. The relationship defines a range of possible proportions of allocation of the first portion of advertisement impressions and the second portion of advertisement impressions. The optimizer generates a solution in accordance with an uncertain supply of advertisement impressions, in which a projected supply of advertisement impressions is determined with a model from the uncertain supply of advertisement impressions.