BACKGROUND

1. Technical Field

The present teaching relates to methods, systems, and programming for content allocation. Specifically, the present teaching relates to allocation of content items to audience groups.

2. Technical Background

In recent years, the percentage of the consumer demographics on the Internet has experienced exponential growth, and continues to grow, thereby fueling the incentive for businesses to advertise online. Optimizing content placement e.g., advertisement (ad) placement to satisfy one or more objectives, however, can be a challenge. For instance, advances in the performance of computer hardware have greatly enhanced the capabilities of servers and networks to obtain and process data with lowered costs. However, the amount of data available to be obtained and processed for optimizing ad placement has grown exponentially in comparison with any advances in hardware performance. Typical advertisement allocation systems focus on optimizing individual campaigns separately due to limitations on computational resources, and the time required to obtain overall enhanced results. Thus, typical content allocation systems in many circumstances obtain significantly less desirable results.

Furthermore, with content suppliers focusing on delivering content to targeted audiences, the usage of data sets that characterize users is becoming increasingly important. However, users typically have multiple data sets that are associated with different data providers. Moreover, each user may have a unique ID corresponding to each data set associated with the respective data provider. Thus, content distributors are frequently unable to efficiently determine the appropriate group of users that should be targeted with particular content.

Therefore, there is a requirement to develop solutions to address such problems.

SUMMARY

The teachings disclosed herein relate to methods, systems, and programming for advertising. More particularly, the present teaching relates to methods, systems, and programming related to exploring sources of advertisement and utilization thereof.

In one example, there is provided a method implemented on a computer having at least one processor, a storage, and a communication platform for determining audience clusters to be assigned to an advertisement exposure opportunity. In accordance with the method, a plurality of advertisement exposure opportunities are obtained, wherein each of the plurality of advertisement exposure opportunities is to be used for placing an advertisement. For each of the plurality of advertisement exposure opportunities, the method includes the steps of generating a plurality of audience clusters based on at least one criterion associated with the advertisement exposure opportunity, ranking the plurality of audience clusters based on at least one ranking criterion, and generating ordered audience clusters based on the ranking. The ordered audience clusters associated with the plurality of advertisement exposure opportunities are updated in response to an event, wherein the ordered audience clusters for each of the plurality of advertisement exposure opportunities are generated prior to receiving a request for placing the advertisement.

In a different example, there is disclosed a system for determining audience clusters to be assigned to an advertisement exposure opportunity. The system comprises an audience data processing unit configured to obtain a plurality of advertisement exposure opportunities, wherein each of the plurality of advertisement exposure opportunities is to be used for placing an advertisement. The system includes a clustering unit and ranking unit. For each of the plurality of advertisement exposure opportunities, the clustering unit is configured to generate a plurality of audience clusters based on at least one criterion associated with the advertisement exposure opportunity, and the ranking unit is configured to rank the plurality of audience clusters based on at least one ranking criterion, and generate ordered audience clusters based on the ranking. The system comprises an updating unit configured to update the ordered audience clusters associated with the plurality of advertisement exposure opportunities in response to an event, wherein the ordered audience clusters for each of the plurality of advertisement exposure opportunities are generated prior to receiving a request for placing the advertisement.

Other concepts relate to software for implementing the present teaching of an optimal content item allocation system. A software product, in accord with this concept, includes at least one machine-readable non-transitory medium and information carried by the medium. The information carried by the medium may be executable program code data, parameters in association with the executable program code, and/or information related to a user, a request, content, or other additional information.

In one example, there is disclosed a non-transitory machine-readable medium having information recorded thereon, wherein the information, when read by the machine, causes the machine to perform the step of: obtaining a plurality of advertisement exposure opportunities, wherein each of the plurality of advertisement exposure opportunities is to be used for placing an advertisement. For each of the plurality of advertisement exposure opportunities, the machine performs the steps of: generating a plurality of audience clusters based on at least one criterion associated with the advertisement exposure opportunity, ranking the plurality of audience clusters based on at least one ranking criterion, and generating ordered audience clusters based on the ranking. The machine performs the step of updating the ordered audience clusters associated with the plurality of advertisement exposure opportunities in response to an event, wherein the ordered audience clusters for each of the plurality of advertisement exposure opportunities are generated prior to receiving a request for placing the advertisement.

Additional advantages and novel features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The advantages of the present teachings may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below.

