| Patent application number | Description | Published |
|---|
| 20080250033 | SYSTEM AND METHOD FOR DETERMINING AN EVENT OCCURENCE RATE - Described are a system and method for determined an event occurrence rate. A sample set of content items may be obtained. Each of the content items may be associated with at least one region in a hierarchical data structure. A first impression volume may be determined for the at least one region as a function of a number of impressions registered for the content items associated with the at least one region. A scale factor may be applied to the first impression volume to generate a second impression volume. The scale factor may be selected so that the second impression volume is within a predefined range of a third impression volume. A click-through-rate (CTR) may be estimated as a function of the second impression volume and a number of clicks on the content item. | 10-09-2008 |
| 20080294577 | Efficient Estimation of Events with Rare Occurrence Rates Using Taxonomies - Methods for predicting the click-through rates of Internet advertisements placed into web pages are disclosed. Specifically, a click-through rate prediction is generating using a hybrid system with two terms. The first term is constructed using a machine learning model that incorporates a limited number of important factors. The second term is constructed using a look-up table that is built using a complex statistical analysis of various web page and advertisement combinations. To construct the second term, the field of multi-level hierarchical modeling is used. Specifically, a tree-structured Markov model is used to process the training data and construct the adjustment factor look-up table. To reduce the complexity of the statistical analysis, Kalman-filters are used to estimate parameters in the traditional multi-level hierarchical models for scalability. | 11-27-2008 |
| 20090043597 | System and method for matching objects using a cluster-dependent multi-armed bandit - An improved system and method for matching objects using a cluster-dependent multi-armed bandit is provided. The matching may be performed by using a multi-armed bandit where the arms of the bandit may be dependent. In an embodiment, a set of objects segmented into a plurality of clusters of dependent objects may be received, and then a two step policy may be employed by a multi-armed bandit by first running over clusters of arms to select a cluster, and then secondly picking a particular arm inside the selected cluster. The multi-armed bandit may exploit dependencies among the arms to efficiently support exploration of a large number of arms. Various embodiments may include policies for discounted rewards and policies for undiscounted reward. These policies may consider each cluster in isolation during processing, and consequently may dramatically reduce the size of a large state space for finding a solution. | 02-12-2009 |
| 20090055139 | PREDICTIVE DISCRETE LATENT FACTOR MODELS FOR LARGE SCALE DYADIC DATA - A method for predicting future responses from large sets of dyadic data includes measuring a dyadic response variable associated with a dyad from two different sets of data; measuring a vector of covariates that captures the characteristics of the dyad; determining one or more latent, unmeasured characteristics that are not determined by the vector of covariates and which induce local structures in a dyadic space defined by the two different sets of data; and modeling a predictive response of the measurements as a function of both the vector of covariates and the one or more latent characteristics, wherein modeling includes employing a combination of regression and matrix co-clustering techniques, and wherein the one or more latent characteristics provide a smoothing effect to the function that produces a more accurate and interpretable predictive model of the dyadic space that predicts future dyadic interaction based on the two different sets of data. | 02-26-2009 |
| 20090063984 | Customized today module - A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time. | 03-05-2009 |
| 20090070177 | System and Method for Optimally Allocating Overlapping Inventory - The present invention introduces methods for allocating: overlapping inventory. In the system of the present invention overlapping inventory problems are reformulated as a network transport problem. Specifically, different inventory types are represented as inventory network nodes. Similarly, corresponding inventory requests are also represented as request network nodes. The different inventory network nodes corresponding to inventory that can satisfy inventory requests are coupled to the request network nodes associated with those inventory requests. A source node is then coupled to the inventory network nodes and a destination node is coupled to the request network nodes. A flow limit of the available inventory is assigned to the connections between the source node and the inventory network nodes. Finally, inventory requests that must be satisfied are represented as flow minimums between the request network nodes and the destination network node. The transport network problem is then solved to solve the corresponding overlapping inventory allocation problem. | 03-12-2009 |
| 20100030717 | FRAMEWORK TO EVALUATE CONTENT DISPLAY POLICIES - Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods. | 02-04-2010 |
| 20110153550 | SYSTEM AND METHOD FOR DETERMINING AN EVENT OCCURRENCE RATE - Described are a system and method for determined an event occurrence rate. A sample set of content items may be obtained. Each of the content items may be associated with at least one region in a hierarchical data structure. A first impression volume may be determined for the at least one region as a function of a number of impressions registered for the content items associated with the at least one region. A scale factor may be applied to the first impression volume to generate a second impression volume. The scale factor may be selected so that the second impression volume is within a predefined range of a third impression volume. A click-through-rate (CTR) may be estimated as a function of the second impression volume and a number of clicks on the content item. | 06-23-2011 |
