| Patent application number | Description | Published |
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| 20100023375 | Fair Allocation of Overlapping Inventory - Methods and apparatus for allocating inventory are disclosed. The system may maintain a plurality of inventory pools of impressions that are projected to be available during a time period, each of the plurality of inventory pools having a corresponding set of attributes. The system may receive a plurality of requests from advertisers, each of the plurality of requests requesting a number of impressions during the time period that satisfy a corresponding demand profile, the demand profile having a corresponding set of attributes. The system may allocate impressions in the plurality of inventory pools to the plurality of requests by balancing an interest of a seller of the impressions to maximize value of remaining inventory in the plurality of inventory pools against an interest of the advertisers in allocating a representative sample of impressions in each of the plurality of inventory pools that satisfy the demand profile for each of the plurality of requests. | 01-28-2010 |
| 20100114710 | SYSTEM AND METHOD FOR FORECASTING AN INVENTORY OF ONLINE ADVERTISEMENT IMPRESSIONS FOR TARGETING IMPRESSION ATTRIBUTES - An improved system and method for forecasting an inventory of online advertisement impressions for targeting profiles of attributes is provided. An index of advertisement impressions on display advertising properties may be built for a targeting profile of attributes from forecasted impression pools. Impression pools of advertisements sharing the same attributes and trend forecast data for web pages and advertisement placements on the web pages may be integrated to generate the forecasted impression pools. An index of several index tables may be generated from forecasted impression pools. A query may be submitted to obtain an inventory forecast of advertisement impressions for targeting profiles of attributes and the index may be searched to match forecasted impression pools for the targeted profile of attributes. Then the inventory forecast of advertisement impressions on display advertising properties may be returned as query results for the targeting profile of attributes. | 05-06-2010 |
| 20100114721 | SYSTEM AND METHOD FOR PRICING OF OVERLAPPING IMPRESSION POOLS OF ONLINE ADVERTISEMENT IMPRESSIONS FOR ADVERTISING DEMAND - An improved system and method for pricing of overlapping impression pools of online advertisement impressions for advertising demand is provided. An inventory of online advertisement impressions may be grouped in impression pools according to attributes of the advertisement impressions and advertisers' requests for impressions targeting specific attributes may be received. An optimal price may be computed for each of the impression pools of the inventory of online advertisement impressions using dual values of an optimization program. The values of a dual variable for prices of impression pools on the supply constraints of an objective function for allocating the impression pools may be extracted and iteratively increased on those impression pools which have a dual value greater than the book rate value. | 05-06-2010 |
| 20100121624 | ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES - Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time. | 05-13-2010 |
| 20100121801 | ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES - Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time. | 05-13-2010 |
| 20100125585 | Conjoint Analysis with Bilinear Regression Models for Segmented Predictive Content Ranking - Information with respect to users, items, and interactions between the users and items is collected. Each user is associated with a set of user features. Each item is associated with a set of item features. An expected score function is defined for each user-item pair, which represents an expected score a user assigns an item. An objective represents the difference between the expected score and the actual score a user assigns an item. The expected score function and the objective function share at least one common variable. The objective function is minimized to find best fit for some of the at least one common variable. Subsequently, the expected score function is used to calculate expected scores for individual users or clusters of users with respect to a set of items that have not received actual scores from the users. The set of items are ranked based on their expected scores. | 05-20-2010 |
| 20100169158 | SQUASHED MATRIX FACTORIZATION FOR MODELING INCOMPLETE DYADIC DATA - A method of predicting a response relationship between elements of two sets includes: specifying a dyadic response matrix; specifying covariates that measure additional dyadic relationships; specifying a number of row clusters and a number of column clusters for clustering the rows and columns of the response matrix; specifying a rank for cluster factors that model average interactions between row clusters and column clusters by products of cluster factors; and determining prediction parameters for predicting responses between elements of the first set and the second set by improving a likelihood value that relates the prediction parameters to the response matrix, the covariates, the observation weights, the row clusters and the column clusters. Determining the prediction parameters includes: updating the prediction parameters for fixed assignments of row clusters and column clusters, and updating assignments for row clusters and column clusters for fixed prediction parameters. | 07-01-2010 |
| 20100217648 | METHOD AND SYSTEM FOR QUANTIFYING USER INTERACTIONS WITH WEB ADVERTISEMENTS - Methods and systems are provided that may be used to determine a probability of whether a visitor to a web document is likely to click on a web advertisement. An exemplary method may include detecting one or more features in a web document. One or more expert statistical models to which the web document belongs may be determined and associated weightings may be determined based, at least in part, on the one or more features detected. A click-through-rate probability for a web advertisement to be placed on the web document may be estimated based on the one or more expert statistical models. | 08-26-2010 |
| 20100241597 | DYNAMIC ESTIMATION OF THE POPULARITY OF WEB CONTENT - Techniques are presented for estimating the current popularity of web content. Click and view data for articles are used to estimate popularity of the articles by analyzing click-through rates. Click-though rates are estimated such that a current click-through rate reflects fluctuations in popularity of articles through time. | 09-23-2010 |
| 20100250556 | Determining User Preference of Items Based on User Ratings and User Features - A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined. Based at least in part on the affinity scores, a candidate item from the set of candidate items is recommended to the user. | 09-30-2010 |
| 20100318432 | ALLOCATION OF INTERNET ADVERTISING INVENTORY - A method for allocating inventory in a networked environment includes receiving a request to purchase a number of display impressions, the request including targeting parameters and a frequency constraint corresponding to a maximum number of times the advertisement can be displayed to a user. The method also includes allocating the requested number of display impressions across a set of user samples, where the number of impressions allocated to any one user sample in the set of user samples is constrained by the frequency constraint. Allocation information that defines how the impressions are allocated among the user samples is stored to a user sample database. | 12-16-2010 |
| 20110099059 | INDEX-BASED TECHNIQUE FRIENDLY CTR PREDICTION AND ADVERTISEMENT SELECTION - Methods and systems are provided for click through rate prediction and advertisement selection in online advertising. Methods are provided in which output information from a feature-based machine learning model is utilized. The output information includes predicted click through rate information. The output information is used to form a matrix. The matrix is modeled using a latent variable model. Machine learning techniques can be used in determining values for unfilled cells of one or more model matrices. The latent variable model can be used in determining predicted click through rate information, and in advertisement selection in connection with serving opportunities. | 04-28-2011 |
