Abstract: A method for processing data on a distributed computing environment is provided. Input data that is to be processed may be stored on an input storage module. Mapper code can be loaded onto a map module and executed. The mapper code can load a mapper executable file onto the map module from a central storage unit and instantiate the mapper executable file. The mapper code, then, can pass the input data to the mapper executable file. The mapper executable file can generate mapped data based on the input data and pass the mapped data back to the mapper code.

Abstract: An improved system and method for granular inventory forecasting of online advertisement impressions is provided. An impression forecast data integrator may be provided that generates forecasted impression pools of advertisements by integrating impression pools of advertisements that share the same attributes and trend forecast data for web pages and advertisement placements on the web pages. Using the trend forecast data, an inventory forecast for a category may be calculated and an inventory forecast for an impression pool may be calculated. A daily forecasted inventory may then be produced for each impression pool by minimizing an objective function of squared errors of the difference between the daily forecasted inventory for each category and the sum of the daily forecasted inventory for each impression pool. The daily inventory forecast for each pool may be output.

Abstract: 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.

Abstract: A method of balancing advertisement inventory allocation includes constructing a flow network of nodes having impressions connected to contracts through corresponding arcs such as to satisfy demand requests of the contracts; normalizing an impression value of each node to a predetermined cost range; setting a cost of each arc to each corresponding normalized value; iteratively performing a plurality of times: (a) sampling the nodes or the arcs to create sample nodes and arcs, each time starting from a different random seed; (b) optimally allocating impressions from the sample nodes to the contracts with a minimum-cost network flow algorithm; (c) separately allocating impressions from sample arcs of lowest cost before allocating those from sample arcs of higher cost; averaging allocations from iterations (b) to create a first allocation; averaging allocations from iterations (c) to produce a second allocation; and computing a weighted solution of the first and second allocations.

Abstract: A method for scaling advertisement inventory allocation includes constructing a flow network of nodes having impressions connected to contracts through corresponding arcs such as to satisfy demand requests of the contracts; (a) for each of the contracts: determining a probability distribution over the nodes eligible to supply forecasted impressions to the contract; drawing a plurality of sample nodes from the probability distribution to form a multiset, O, of nodes; (b) for each of the nodes within O: determining a subset of the contracts, H, that can be satisfied by receiving forecasted impressions from the node; weighting a number of forecasted impressions of the node, as a function of the subset of contracts in H, with the probability distribution of the node; and optimally allocating forecasted impressions from each multiset, O, of sample nodes to each corresponding contract during the time period by solving the flow network with a minimum-cost network flow algorithm.

Abstract: A method for scaling inventory allocation includes mapping attributes to impressions through index tables; constructing a flow network of nodes each containing impressions of corresponding attributes projected to be available during a time period, contracts each including specific requests for impressions that satisfy a demand profile, and arcs to connect the nodes to the contracts that match the demand profiles of the contracts; sampling the arcs that flow into each contract at a sampling rate chosen to reduce the number of arcs to a fraction of the original arcs when the plurality of impressions that satisfy the contract is above a threshold number, the nodes corresponding to the sampled arcs being sampled nodes; and optimally allocating impressions from the sampled nodes to the contracts during the time period by solving the flow network with a minimum-cost network flow algorithm that maximizes delivery of the impressions from the sampled nodes to the contracts in a way that satisfies the corresponding dem

Abstract: A system for advertisement inventory allocation is disclosed, including a database to store advertisement impressions. An indexer builds a plurality of index tables each associated with an attribute that is mapped to a plurality of the impressions. An impression matcher constructs a flow network including a plurality of nodes each containing impressions of at least one corresponding attribute projected to be available during a time period, a plurality of contracts each including specific requests for impressions that satisfy a demand profile during the time period, and a plurality of arcs to connect the plurality of nodes to the plurality of contracts that match the demand profile of each contract.

Abstract: A computer implemented system includes a computer readable storage medium which includes historical demand data for a plurality of advertising inventories, and a processor connected to the computer readable storage medium. The processor is configured for generating a first demand forecast for a first predetermined period of time and a second demand forecast for a second predetermined period of time. The processor is configured for adjusting the first demand forecast by removing an existing demand for each of the plurality of advertising inventories, and for generating a net forecasting demand for each of the plurality of inventories for a third predetermined period of time by combining the second demand forecast and an adjusted first demand forecast. The third predetermined period of time is based on the first and second predetermined periods.

Abstract: Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Abstract: Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Abstract: Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Abstract: Systems and methods are provided for predicting visitor traffic to a network of web site pages. The systems and methods are used, as an example, to predict the inventory of total available online advertisements available within the network for a forthcoming period. The visitor traffic includes page viewing, listening or transacting on web pages within a web site, wherein the web pages are categorized by subject, interest areas or specific user queries such as word or phrase searches. For each page whose traffic is being predicted, the system takes into account annual seasonality, day-of-week, holidays, special events, short histories, user demographics, user web behavior (viewing, listening and transacting) and parent and child web page characteristics.

Abstract: Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Abstract: Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Abstract: Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Abstract: A method and apparatus for selecting additional content to display to a user when the user requests base content is provided. A user profile of the user having user interest scores of categories or keywords is received, each user interest score reflecting the degree of interest the user has in the category or keyword. Performance scores reflecting the probability that a user having particular user interest scores will select additional content associated with particular categories or keywords is also received. In addition, revenue amounts associated with each category or keyword of the user profile is received. The user interest scores, performance scores, and revenue amounts are used to produce an expected revenue amount for each category or keyword in the user profile. Additional content to be sent to the user is then selected using the determined expected revenue amounts.